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Journal articles on the topic 'Chewing episodes detection algorithm'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

Blechert, Jens, Michael Liedlgruber, Anja Lender, Julia Reichenberger, and Frank H. Wilhelm. "Unobtrusive electromyography-based eating detection in daily life: A new tool to address underreporting?" Appetite 118 (August 7, 2017): 168–73. https://doi.org/10.1016/j.appet.2017.08.008.

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Research on eating behavior is limited by an overreliance on self-report. It is well known that actual food intake is frequently underreported, and it is likely that this problem is overrepresented in vulnerable populations. The present research tested a chewing detection method that could assist self-report methods. A trained sample of 15 participants (usable data of 14 participants) kept detailed eating records during one day and one night while carrying a recording device. Signals recorded from electromyography sensors unobtrusively placed behind the right ear were used to develop a chewing

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Zhang, Rui, and Oliver Amft. "Retrieval and Timing Performance of Chewing-Based Eating Event Detection in Wearable Sensors." Sensors 20, no. 2 (2020): 557. http://dx.doi.org/10.3390/s20020557.

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We present an eating detection algorithm for wearable sensors based on first detecting chewing cycles and subsequently estimating eating phases. We term the corresponding algorithm class as a bottom-up approach. We evaluated the algorithm using electromyographic (EMG) recordings from diet-monitoring eyeglasses in free-living and compared the bottom-up approach against two top-down algorithms. We show that the F1 score was no longer the primary relevant evaluation metric when retrieval rates exceeded approx. 90%. Instead, detection timing errors provided more important insight into detection pe

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Alshboul, Sana, and Mohammad Fraiwan. "Determination of Chewing Count from Video Recordings Using Discrete Wavelet Decomposition and Low Pass Filtration." Sensors 21, no. 20 (2021): 6806. http://dx.doi.org/10.3390/s21206806.

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Several studies have shown the importance of proper chewing and the effect of chewing speed on the human health in terms of caloric intake and even cognitive functions. This study aims at designing algorithms for determining the chew count from video recordings of subjects consuming food items. A novel algorithm based on image and signal processing techniques has been developed to continuously capture the area of interest from the video clips, determine facial landmarks, generate the chewing signal, and process the signal with two methods: low pass filter, and discrete wavelet decomposition. P

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Li, Jinxing, Yanhong Liu, Wenxin Zheng, Xinwen Chen, Yabin Ma, and Leifeng Guo. "Monitoring Cattle Ruminating Behavior Based on an Improved Keypoint Detection Model." Animals 14, no. 12 (2024): 1791. http://dx.doi.org/10.3390/ani14121791.

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Cattle rumination behavior is strongly correlated with its health. Current methods often rely on manual observation or wearable devices to monitor ruminating behavior. However, the manual monitoring of cattle rumination is labor-intensive, and wearable devices often harm animals. Therefore, this study proposes a non-contact method for monitoring cattle rumination behavior, utilizing an improved YOLOv8-pose keypoint detection algorithm combined with multi-condition threshold peak detection to automatically identify chewing counts. First, we tracked and recorded the cattle’s rumination behavior

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Gao, Ronghua, Qihang Liu, Qifeng Li, et al. "Multi-Target Rumination Behavior Analysis Method of Cows Based on Target Detection and Optical Flow Algorithm." Sustainability 15, no. 18 (2023): 14015. http://dx.doi.org/10.3390/su151814015.

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Rumination behavior is closely associated with factors such as cow productivity, reproductive performance, and disease incidence. For multi-object scenarios of dairy cattle, ruminant mouth area images accounted for little characteristic information, which was first put forward using an improved Faster R-CNN target detection algorithm to improve the detection performance model for the ruminant area of dairy cattle. The primary objective is to enhance the model’s performance in accurately detecting cow rumination regions. To achieve this, the dataset used in this study is annotated with both the

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Vaquera-Aparicio, Denisse, Abiel Mascareñas-de los Santos, José Castillo-Bejarano, Daniela Cisneros-Saldaña, Eduardo Romo-Leija, and Julia Colunga-Pedraza. "Impact of the Implementation of a Fever and Neutropenia Management Algorithm in the Detection of Microbiologically Confirmed Infections in Mexican Children." Journal of the Pediatric Infectious Diseases Society 12, Supplement_1 (2023): S12—S13. http://dx.doi.org/10.1093/jpids/piad070.023.

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Abstract Background Fever and neutropenia is a medical emergency in children with cancer. Microbiological identification of the febrile focus is estimated to occur in 20-30% of cases. Algorithms that implement evaluation and treatment strategies help institutions to standardize the appropriate clinical and microbiological diagnosis and management of these episodes, thus improving the rate of confirmed infections. In this study we aimed to report the impact in the identification rates of microbiologically confirmed infections before and after implementing a management algorithm for children wit

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Micó-Amigo, M. Encarna, Idsart Kingma, Erik Ainsworth, et al. "A novel accelerometry-based algorithm for the detection of step durations over short episodes of gait in healthy elderly." Journal of NeuroEngineering and Rehabilitation 13, no. 1 (2016): 38. https://doi.org/10.1186/s12984-016-0145-6.

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<strong>Background: </strong>The assessment of short episodes of gait is clinically relevant and easily implemented, especially given limited space and time requirements. BFS (body-fixed-sensors) are small, lightweight and easy to wear sensors, which allow the assessment of gait at relative low cost and with low interference. Thus, the assessment with BFS of short episodes of gait, extracted from dailylife physical activity or measured in a standardised and supervised setting, may add value in the study of gait quality of the elderly. The aim of this study was to evaluate the accuracy of a nov

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Reich, DL, C. Grubb, and R. Roth. "AN ALGORITHM FOR THE DETECTION OF EPISODES OF INTRAOPERATIVE LIGHT ANESTHESIA." Anesthesia & Analgesia 86, Supplement (1998): 225S. http://dx.doi.org/10.1097/00000539-199802001-00223.

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Linna, Du, Liu Enwei, Wang Jiaxia, and Cui Yifeng. "Analysis of Oral Carcinogenesis Factors Based on Non Negative Matrix Evolutionary Spectrum Clustering Decomposition." Journal of Medical Imaging and Health Informatics 10, no. 9 (2020): 2016–21. http://dx.doi.org/10.1166/jmihi.2020.3126.

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To explore the risk factors of Oral Submucosal fibrous carcinogenesis and provide theoretical basis for selfprevention and clinical treatment. First of all, an evolutionary non negative matrix factorization framework based on clustering quality is proposed for dynamic oral carcinogenesis detection. Then, the equivalence among evolutionary spectral clustering, ENMF and density optimization of evolutionary modules is proved theoretically. At the same time, based on this equivalence, a new semi-supervised ENMF algorithm is proposed by adding a priori information to ENMF without increasing the tim

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Wang, Ning, Xincheng Li, Shuqi Shang, Yuliang Yun, Zeyang Liu, and Deyang Lyu. "Monitoring Dairy Cow Rumination Behavior Based on Upper and Lower Jaw Tracking." Agriculture 14, no. 11 (2024): 2006. http://dx.doi.org/10.3390/agriculture14112006.

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To address behavioral interferences such as head turning and lowering during rumination in group-housed dairy cows, an enhanced network algorithm combining the YOLOv5s and DeepSort algorithms was developed. Initially, improvements were made to the YOLOv5s algorithm by incorporating the C3_CA module into the backbone to enhance the feature interaction and representation at different levels. The Slim_Neck paradigm was employed to strengthen the feature extraction and fusion, and the CIoU loss function was replaced with the WIoU loss function to improve the model’s robustness and generalization,

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Marcante, Andrea, Roberto Di Marco, Giovanni Gentile, et al. "Foot Pressure Wearable Sensors for Freezing of Gait Detection in Parkinson’s Disease." Sensors 21, no. 1 (2020): 128. http://dx.doi.org/10.3390/s21010128.

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Freezing of Gait (FoG) is a common symptom in Parkinson’s Disease (PD) occurring with significant variability and severity and is associated with increased risk of falls. FoG detection in everyday life is not trivial, particularly in patients manifesting the symptom only in specific conditions. Various wearable devices have been proposed to detect PD symptoms, primarily based on inertial sensors. We here report the results of the validation of a novel system based on a pair of pressure insoles equipped with a 3D accelerometer to detect FoG episodes. Twenty PD patients attended a motor assessme

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Beiderman, Yevgeny, Mark Kunin, Eli Kolberg, et al. "Automatic solution for detection, identification and biomedical monitoring of a cow using remote sensing for optimised treatment of cattle." Journal of Agricultural Engineering 45, no. 4 (2014): 153. http://dx.doi.org/10.4081/jae.2014.418.

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In this paper we show how a novel photonic remote sensing system assembled on a robotic platform can extract vital biomedical parameters from cattle including their heart beating, breathing and chewing activity. The sensor is based upon a camera and a laser using selfinterference phenomena. The whole system intends to provide an automatic solution for detection, identification and biomedical monitoring of a cow. The detection algorithm is based upon image processing involving probability map construction. The identification algorithms involve well known image pattern recognition techniques. Th

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Farnell, Carme, and Tomeu Rigo. "The Lightning Jump Algorithm for Nowcasting Convective Rainfall in Catalonia." Atmosphere 11, no. 4 (2020): 397. http://dx.doi.org/10.3390/atmos11040397.

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Previous studies in Catalonia (NE Iberian Peninsula) showed a direct relationship between the Lightning Jump (LJ) and severe weather, from the study of different events, occurring in the last few years in this region. This research goes a step beyond by studying the relationship between LJ and heavy rainfall, considering different criteria. It selects those episodes exceeding the 40 mm/h threshold, dividing them between those with or without LJ occurrence (3760 and 14,238 cases, respectively). The time and distance criteria (<150 km and <50 min, respectively) allow the detection of rainf

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BÁLYA, DAVID. "SUDDEN GLOBAL SPATIAL-TEMPORAL CHANGE DETECTION AND ITS APPLICATIONS." Journal of Circuits, Systems and Computers 12, no. 06 (2003): 845–56. http://dx.doi.org/10.1142/s0218126603001173.

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We are watching the news on TV: the change of the background tells us when a new story begins. A glance at the clock and we can clearly see what time it is. These are special spatial-temporal episodes caused by ballistic eye movements and sudden optical changes. In this paper we give a useful definition for generalized sudden global change events, present its main properties and give an algorithm to recognize them in any video-flow. The proposed algorithm is implemented on a standard Cellular Nonlinear Network Universal Machine (CNN-UM). The processing time of the detection is roughly one mill

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Bouchard-Roy, Jacob, Aidin Delnavaz, and Jérémie Voix. "Mobile In-Ear Power Sensor for Jaw Joint Activity." Micromachines 11, no. 12 (2020): 1047. http://dx.doi.org/10.3390/mi11121047.

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In only a short time, in-ear wearables have gone from hearing aids to a host of electronic devices such as wireless earbuds and digital earplugs. To operate, these devices rely exclusively on batteries, which are not only cumbersome but known for several drawbacks. In this paper, the earcanal dynamic movements generated by jaw activity are evaluated as an alternative source of energy that could replace batteries. A mobile in-ear power sensor device capable of measuring jaw activity metrics is prototyped and tested on three test subjects. The test results are subsequently analyzed using a detec

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Gogus, Fatma Zehra, and Gulay Tezel. "Apneic Events Detection Using Different Features of Airflow Signals." January 2019 38, no. 1 (2019): 1–16. http://dx.doi.org/10.22581/muet1982.1901.01.

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Apneic-event based sleep disorders are very common and affect greatly the daily life of people. However, diagnosis of these disorders by detecting apneic events are very difficult. Studies show that analyzes of airflow signals are effective in diagnosis of apneic-event based sleep disorders. According to these studies, diagnosis can be performed by detecting the apneic episodes of the airflow signals. This work deals with detection of apneic episodes on airflow signals belonging to Apnea-ECG (Electrocardiogram) and MIT (Massachusetts Institute of Technology) BIH (Bastons’s Beth Isreal Hospital

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Drzazga, Jakub, and Bogusław Cyganek. "An LSTM Network for Apnea and Hypopnea Episodes Detection in Respiratory Signals." Sensors 21, no. 17 (2021): 5858. http://dx.doi.org/10.3390/s21175858.

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One of the most common sleep disorders is sleep apnea. It manifests itself by episodes of shallow breathing or pauses in breathing during the night. Diagnosis of this disease involves polysomnography examination, which is expensive. Alternatively, diagnostic doctors can be supported with recordings from the in-home polygraphy sensors. Furthermore, numerous attempts for providing an automated apnea episodes annotation algorithm have been made. Most of them, however, do not distinguish between apnea and hypopnea episodes. In this work, a novel solution for epoch-based annotation problem is prese

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Wu, Ming-Feng, Wei-Chang Huang, Kai-Ming Chang, et al. "Detection Performance Regarding Sleep Apnea-Hypopnea Episodes with Fuzzy Logic Fusion on Single-Channel Airflow Indexes." Applied Sciences 10, no. 5 (2020): 1868. http://dx.doi.org/10.3390/app10051868.

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Obstructive sleep apnea-hypopnea syndrome (OSAHS) affects more than 936 million people worldwide and is the most common sleep-related breathing disorder; almost 80% of potential patients remain undiagnosed. To treat moderate to severe OSAHS as early as possible, the use of fewer sensing channels is recommended to screen for OSAHS and shorten waiting lists for the gold standard polysomnography (PSG). Hence, an effective out-of-clinic detection method may provide a solution to hospital overburden and associated health care costs. Applying single-channel signals to simultaneously detect apnea and

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Ling, S. S. H., and H. T. Nguyen. "Genetic-Algorithm-Based Multiple Regression With Fuzzy Inference System for Detection of Nocturnal Hypoglycemic Episodes." IEEE Transactions on Information Technology in Biomedicine 15, no. 2 (2011): 308–15. http://dx.doi.org/10.1109/titb.2010.2103953.

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Nguyen, Trong Tuyen, Trong Huu Tran, Mau Thach Nguyen, and Z. M. Yuldashev. "Heart Rhythm Intelligent Monitoring Algorithm for the Atrial Fibrillation Episodes Detection in the Telemedicine System." Procedia Computer Science 150 (2019): 352–60. http://dx.doi.org/10.1016/j.procs.2019.02.063.

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Haussler, Allison M., Lauren E. Tueth, David S. May, Gammon M. Earhart, and Pietro Mazzoni. "Refinement of an Algorithm to Detect and Predict Freezing of Gait in Parkinson Disease Using Wearable Sensors." Sensors 25, no. 1 (2024): 124. https://doi.org/10.3390/s25010124.

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Freezing of gait (FOG) is a debilitating symptom of Parkinson disease (PD). It is episodic and variable in nature, making assessment difficult. Wearable sensors used in conjunction with specialized algorithms, such as our group’s pFOG algorithm, provide objective data to better understand this phenomenon. While these methods are effective at detecting FOG retrospectively, more work is needed. The purpose of this paper is to explore how the existing pFOG algorithm can be refined to improve the detection and prediction of FOG. To accomplish this goal, previously collected data were utilized to a

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Punin, Catalina, Boris Barzallo, Roger Clotet, et al. "A Non-Invasive Medical Device for Parkinson’s Patients with Episodes of Freezing of Gait." Sensors 19, no. 3 (2019): 737. http://dx.doi.org/10.3390/s19030737.

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A critical symptom of Parkinson’s disease (PD) is the occurrence of Freezing of Gait (FOG), an episodic disorder that causes frequent falls and consequential injuries in PD patients. There are various auditory, visual, tactile, and other types of stimulation interventions that can be used to induce PD patients to escape FOG episodes. In this article, we describe a low cost wearable system for non-invasive gait monitoring and external delivery of superficial vibratory stimulation to the lower extremities triggered by FOG episodes. The intended purpose is to reduce the duration of the FOG episod

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Hayano, Junichiro, Hiroaki Yamamoto, Izumi Nonaka, et al. "Quantitative detection of sleep apnea with wearable watch device." PLOS ONE 15, no. 11 (2020): e0237279. http://dx.doi.org/10.1371/journal.pone.0237279.

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The spread of wearable watch devices with photoplethysmography (PPG) sensors has made it possible to use continuous pulse wave data during daily life. We examined if PPG pulse wave data can be used to detect sleep apnea, a common but underdiagnosed health problem associated with impaired quality of life and increased cardiovascular risk. In 41 patients undergoing diagnostic polysomnography (PSG) for sleep apnea, PPG was recorded simultaneously with a wearable watch device. The pulse interval data were analyzed by an automated algorithm called auto-correlated wave detection with adaptive thresh

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Kumar, Amit, and Mandeep Singh. "Statistical analysis of ST segments in ECG signals for detection of ischaemic episodes." Transactions of the Institute of Measurement and Control 40, no. 3 (2016): 819–30. http://dx.doi.org/10.1177/0142331216667811.

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This paper highlights a new method for the detection of ischaemic episodes using statistical features derived from ST segment deviations in electrocardiogram (ECG) signal. Firstly, ECG records are pre-processed for the removal of artifacts followed by the delineation process. Then region of interest (ROI) is defined for ST segment and isoelectric reference to compute the ST segment deviation. The mean thresholds for ST segment deviations are used to differentiate the ischaemic beats from normal beats in two stages. The window characterization algorithm is developed for filtration of spurious b

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Trong Tuyen, N., H. Thi Yen, N. Manh Cuong, et al. "Atrial fibrillation detection using machine learning algorithm from single lead electrocardiograms." Journal of Arrhythmology 32, no. 2 (2025): 27–32. https://doi.org/10.35336/va-1493.

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Aim. Atrial fibrillation (AF) represents one of the most critical cardiac arrhythmias, as it significantly increases the risk of stroke. Its detection is particularly challenging due to the unpredictable nature of its episodes.Methods. This study proposes a low-complexity algorithm, enabling integration into embedded devices for realtime AF episode detection. The proposed method integrates non-linear, time-domain and frequency-domain features extracted from electrocardiogram signals with The LightGBM algorithm (an extension of decision tree algorithm) is used to classify and detect AF.Results.

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Guarnido-Lopez, Pablo, John-Fredy Ramirez-Agudelo, Emmanuel Denimal, and Mohammed Benaouda. "Programming and Setting Up the Object Detection Algorithm YOLO to Determine Feeding Activities of Beef Cattle: A Comparison between YOLOv8m and YOLOv10m." Animals 14, no. 19 (2024): 2821. http://dx.doi.org/10.3390/ani14192821.

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This study highlights the importance of monitoring cattle feeding behavior using the YOLO algorithm for object detection. Videos of six Charolais bulls were recorded on a French farm, and three feeding behaviors (biting, chewing, visiting) were identified and labeled using Roboflow. YOLOv8 and YOLOv10 were compared for their performance in detecting these behaviors. YOLOv10 outperformed YOLOv8 with slightly higher precision, recall, mAP50, and mAP50-95 scores. Although both algorithms demonstrated similar overall accuracy (around 90%), YOLOv8 reached optimal training faster and exhibited less

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Fayad, Ibrahim, Nicolas Baghdadi, Hassan Bazzi, and Mehrez Zribi. "Near Real-Time Freeze Detection over Agricultural Plots Using Sentinel-1 Data." Remote Sensing 12, no. 12 (2020): 1976. http://dx.doi.org/10.3390/rs12121976.

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Short-term freeze/thaw cycles, which mostly occur in the northern hemisphere across the majority of land surfaces, are reported to cause severe economic losses over broad areas of Europe and North America. Therefore, in order to assess the extent of frost damage in the agricultural sector, the objective of this study is to build an operational approach capable of detecting frozen plots at the plot scale in a near real-time scenario using Sentinel-1 (S1) data. C-band synthetic aperture radar (SAR) data show high potential for the detection of freeze/thaw surface states due to the significant al

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Adjei, Tricia, Ryan Purdy, João Jorge, et al. "New method to measure interbreath intervals in infants for the assessment of apnoea and respiration." BMJ Open Respiratory Research 8, no. 1 (2021): e001042. http://dx.doi.org/10.1136/bmjresp-2021-001042.

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BackgroundRespiratory disorders, including apnoea, are common in preterm infants due to their immature respiratory control compared with term-born infants. However, our inability to accurately measure respiratory rate in hospitalised infants results in unreported episodes of apnoea and an incomplete picture of respiratory activity.MethodsWe develop, validate and use a novel algorithm to identify interbreath intervals (IBIs) and apnoeas in preterm infants. In 42 preterm infants (1600 hours of recordings), we assess IBIs from the chest electrical impedance pneumograph using an adaptive amplitude

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Cinotti, Eliana, Jessica Centracchio, Salvatore Parlato, et al. "A Narrowband IoT Personal Sensor for Long-Term Heart Rate Monitoring and Atrial Fibrillation Detection." Sensors 24, no. 14 (2024): 4432. http://dx.doi.org/10.3390/s24144432.

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Long-term patient monitoring is required for detection of episodes of atrial fibrillation, one of the most widespread cardiac pathologies. Today, the most used non-invasive technique is Holter electrocardiographic (ECG) monitoring, which can often prove ineffective because of the short duration of recordings (e.g., one day). Other techniques such as photo-plethysmography are adopted by smartwatches for much longer duration monitoring, but this has the disadvantage of offering only intermittent measurements. This study proposes an Internet of Things (IoT) sensor that can provide a very long per

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Gonzalez-Bermejo, Jesus, Jean-Paul Janssens, Claudio Rabec, et al. "Framework for patient-ventilator asynchrony during long-term non-invasive ventilation." Thorax 74, no. 7 (2019): 715–17. http://dx.doi.org/10.1136/thoraxjnl-2018-213022.

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Episodes of patient-ventilator asynchrony (PVA) occur during acute and chronic non-invasive positive pressure ventilation (NIV). In long-term NIV, description and quantification of PVA is not standardised, thus limiting assessment of its clinical impact. The present report provides a framework for a systematic analysis of polygraphic recordings of patients under NIV for the detection and classification of PVA validated by bench testing. The algorithm described uses two different time windows: rate asynchrony and intracycle asynchrony. This approach should facilitate further studies on prevalen

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Oberhettinger, Philipp, Jan Zieger, Ingo Autenrieth, Matthias Marschal, and Silke Peter. "Evaluation of two rapid molecular test systems to establish an algorithm for fast identification of bacterial pathogens from positive blood cultures." European Journal of Clinical Microbiology & Infectious Diseases 39, no. 6 (2020): 1147–57. http://dx.doi.org/10.1007/s10096-020-03828-5.

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Abstract Fast identification of pathogens directly from positive blood cultures is of highest importance to supply an adequate therapy of bloodstream infections (BSI). There are several platforms providing molecular-based identification, detection of antimicrobial resistance genes, or even a full antimicrobial susceptibility testing (AST). Two of such test systems allowing rapid diagnostics were assessed in this study: The Biofire FilmArray® and the Genmark ePlex®, both fully automated test system with a minimum of hands-on time. Overall 137 BSI episodes were included in our study and compared

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Han, Dong, Syed Khairul Bashar, Fahimeh Mohagheghian, et al. "Premature Atrial and Ventricular Contraction Detection Using Photoplethysmographic Data from a Smartwatch." Sensors 20, no. 19 (2020): 5683. http://dx.doi.org/10.3390/s20195683.

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We developed an algorithm to detect premature atrial contraction (PAC) and premature ventricular contraction (PVC) using photoplethysmographic (PPG) data acquired from a smartwatch. Our PAC/PVC detection algorithm is composed of a sequence of algorithms that are combined to discriminate various arrhythmias. A novel vector resemblance method is used to enhance the PAC/PVC detection results of the Poincaré plot method. The new PAC/PVC detection algorithm with our automated motion and noise artifact detection approach yielded a sensitivity of 86% for atrial fibrillation (AF) subjects while the ov

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Abisoye, Opeyemi Aderiike. "An Improved Myocardial Infarction Detection using Convolutional Neural Network and Graph Neural Network Algorithm." Computer Engineering and Applications Journal 13, no. 2 (2024): 20–29. http://dx.doi.org/10.18495/comengapp.v13i2.438.

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Myocardial infarction (MI) is a crucial health problem and its mortality rate is higher than that of cancer. It is the damage and death of heart muscle from the sudden blockage of a coronary artery by a blood clot. Although lots of researches have been carried out with impressive performance record for detection of MI, however, existing approaches for MI detection can be improved upon for better performance. A vital piece of medical technology that aids in the diagnosis of a number of heart-related disorders in patients is an electrocardiogram (ECG). To find significant episodes in long-term E

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Veldhuis, J. D., V. Guardabasso, A. D. Rogol, et al. "Appraising the nature of luteinizing hormone secretory events in men." American Journal of Physiology-Endocrinology and Metabolism 252, no. 5 (1987): E599—E605. http://dx.doi.org/10.1152/ajpendo.1987.252.5.e599.

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We have appraised the nature of spontaneous luteinizing hormone (LH) secretory events in normal men by analyzing immunoactive LH concentrations in blood samples withdrawn at 5-min intervals for 24 h in eight healthy individuals. A novel discrete deconvolution algorithm was applied to determine apparent instantaneous LH secretory rates from these LH concentration series. These analyses unmasked unique attributes of spontaneous LH secretory events, which were represented as delimited momentary augmentations in endogenous LH secretory rates interspersed among intervals of relative secretory quies

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Lee, Chien-Cheng, Ping-Wing Lui, Wei-Wei Gao, and Zhongjian Gao. "Detection of Rat Pain-Related Grooming Behaviors Using Multistream Recurrent Convolutional Networks on Day-Long Video Recordings." Bioengineering 11, no. 12 (2024): 1180. http://dx.doi.org/10.3390/bioengineering11121180.

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In experimental pain studies involving animals, subjective pain reports are not feasible. Current methods for detecting pain-related behaviors rely on human observation, which is time-consuming and labor-intensive, particularly for lengthy video recordings. Automating the quantification of these behaviors poses substantial challenges. In this study, we developed and evaluated a deep learning, multistream algorithm to detect pain-related grooming behaviors in rats. Pain-related grooming behaviors were induced by injecting small amounts of pain-inducing chemicals into the rats’ hind limbs. Day-l

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N. Juber Rahman. "An Analytics of Sleep Apnea Classification using Caswideresnet Algorithm." Communications on Applied Nonlinear Analysis 31, no. 2 (2024): 270–83. http://dx.doi.org/10.52783/cana.v31.550.

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Sleep apnea is a common yet serious sleep disorder that affects millions of individuals worldwide. Timely and accurate detection of sleep apnea can significantly improve patient outcomes and quality of life. In this study, we propose an advanced approach for sleep apnea classification and prediction using the CasWideResNet algorithm. Our methodology includes preprocessing steps such as Z-score normalization to standardize the data and improve algorithm performance. Additionally, we employ an improved Pan-Tompkins algorithm for feature selection, which helps in identifying relevant features fro

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Chen, Hongyu, Zaihao Wang, Chunmei Lu, et al. "Neonatal Seizure Detection Using a Wearable Multi-Sensor System." Bioengineering 10, no. 6 (2023): 658. http://dx.doi.org/10.3390/bioengineering10060658.

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Neonatal seizure is an important clinical symptom of brain dysfunction, which is more common in infancy than in childhood. At present, video electroencephalogram (VEEG) technology is widely used in clinical practice. However, video electroencephalogram technology has several disadvantages. For example, the wires connecting the medical instruments may interfere with the infant’s movement and the gel patch electrode or disk electrode commonly used to monitor EEG may cause skin allergies or even tears. For the above reasons, we developed a wearable multi-sensor platform for newborns to collect ph

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Zhang, Yinchao, Su Chen, Siying Chen, He Chen, and Pan Guo. "A novel lidar gradient cluster analysis method of nocturnal boundary layer detection during air pollution episodes." Atmospheric Measurement Techniques 13, no. 12 (2020): 6675–89. http://dx.doi.org/10.5194/amt-13-6675-2020.

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Abstract. The observation of the nocturnal boundary layer height (NBLH) plays an important role in air pollution and monitoring. Through 39 d of heavy pollution observation experiments in Beijing (China), as well as an exhaustive evaluation of the gradient, wavelet covariance transform, and cubic root gradient methods, a novel algorithm based on the cluster analysis of the gradient method (CA-GM) of lidar signals is developed to capture the multilayer structure and achieve night-time stability. The CA-GM highlights its performance compared with radiosonde data, and the best correlation (0.85),

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Lin, Michael Y., Keith F. Woeltje, Yosef M. Khan, et al. "Multicenter Evaluation of Computer Automated versus Traditional Surveillance of Hospital-Acquired Bloodstream Infections." Infection Control & Hospital Epidemiology 35, no. 12 (2014): 1483–90. http://dx.doi.org/10.1086/678602.

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Objective.Central line–associated bloodstream infection (BSI) rates are a key quality metric for comparing hospital quality and safety. Traditional BSI surveillance may be limited by interrater variability. We assessed whether a computer-automated method of central line–associated BSI detection can improve the validity of surveillance.Design.Retrospective cohort study.Setting.Eight medical and surgical intensive care units (ICUs) in 4 academic medical centers.Methods.Traditional surveillance (by hospital staff) and computer algorithm surveillance were each compared against a retrospective audi

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Zhang, Hui, Jie Zhang, Hong-Bao Li, et al. "Validation of Single Centre Pre-Mobile Atrial Fibrillation Apps for Continuous Monitoring of Atrial Fibrillation in a Real-World Setting: Pilot Cohort Study." Journal of Medical Internet Research 21, no. 12 (2019): e14909. http://dx.doi.org/10.2196/14909.

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Background Atrial fibrillation is the most common recurrent arrhythmia in clinical practice, with most clinical events occurring outside the hospital. Low detection and nonadherence to guidelines are the primary obstacles to atrial fibrillation management. Photoplethysmography is a novel technology developed for atrial fibrillation screening. However, there has been limited validation of photoplethysmography-based smart devices for the detection of atrial fibrillation and its underlying clinical factors impacting detection. Objective This study aimed to explore the feasibility of photoplethysm

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Borzì, Luigi, Ivan Mazzetta, Alessandro Zampogna, Antonio Suppa, Gabriella Olmo, and Fernanda Irrera. "Prediction of Freezing of Gait in Parkinson’s Disease Using Wearables and Machine Learning." Sensors 21, no. 2 (2021): 614. http://dx.doi.org/10.3390/s21020614.

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Freezing of gait (FOG) is one of the most troublesome symptoms of Parkinson’s disease, affecting more than 50% of patients in advanced stages of the disease. Wearable technology has been widely used for its automatic detection, and some papers have been recently published in the direction of its prediction. Such predictions may be used for the administration of cues, in order to prevent the occurrence of gait freezing. The aim of the present study was to propose a wearable system able to catch the typical degradation of the walking pattern preceding FOG episodes, to achieve reliable FOG predic

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Wang, Xiaoshuai, Binghong Chen, Ruimin Yang, Kai Liu, Kaixuan Cuan, and Mengbing Cao. "A Non-Contact and Fast Estimating Method for Respiration Rate of Cows Using Machine Vision." Agriculture 14, no. 1 (2023): 40. http://dx.doi.org/10.3390/agriculture14010040.

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Detecting respiration rate (RR) is a promising and practical heat stress diagnostic method for cows, with significant potential benefits for dairy operations in monitoring thermal conditions and managing cooling treatments. Currently, the optical flow method is widely employed for automatic video-based RR estimation. However, the optical flow-based approach for RR estimation can be time-consuming and susceptible to interference from various unrelated cow movements, such as rising, lying down, and body shaking. The aim of this study was to propose a novel optical flow-based algorithm for remote

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Weipert, Kay F., Srdjan Kostic, Timur Gökyildirim, et al. "Safety and Performance of the Subcutaneous Implantable Cardioverter Defibrillator Detection Algorithm INSIGHTTM in Pacemaker Patients." Journal of Clinical Medicine 13, no. 1 (2023): 129. http://dx.doi.org/10.3390/jcm13010129.

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Background: The use of the S-ICD is limited by its inability to provide backup pacing. Combined use of the S-ICD with a pacemaker may be a good choice in certain situations, yet current experience concerning the compatibility is limited. The goal of this study was to determine the safety and efficacy of the S-ICD in patients with a pacemaker. Methods: A total of 74 consecutive patients with a bipolar pacemaker were prospectively enrolled. First, surface rhythm strips were recorded in all possible pacemaker stimulation modes, to screen for T-wave oversensing (TWOS). Second, a S-ICD functional d

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Suda, Satoshi, Takehiro Katano, Kazuo Kitagawa, et al. "Detection of Atrial Fibrillation Using Insertable Cardiac Monitors in Patients With Cryptogenic Stroke in Japan (the LOOK Study): Protocol for a Prospective Multicenter Observational Study." JMIR Research Protocols 12 (April 13, 2023): e39307. http://dx.doi.org/10.2196/39307.

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Background Paroxysmal atrial fibrillation (AF) is a probable cause of cryptogenic stroke (CS), and its detection and treatment are important for the secondary prevention of stroke. Insertable cardiac monitors (ICMs) are clinically effective in screening for AF and are superior to conventional short-term cardiac monitoring. Japanese guidelines for determining clinical indications for ICMs in CS are stricter than those in Western countries. Differences between Japanese and Western guidelines may impact the detection rate and prediction of AF via ICMs in patients with CS. Available data on Japane

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Mariani, Marco Valerio, Carlo Lavalle, Giovanni Battista Forleo, et al. "HeartLogic™: real-world data—efficiency, resource consumption, and workflow optimization." European Heart Journal Supplements 25, Supplement_C (2023): C331—C336. http://dx.doi.org/10.1093/eurheartjsupp/suad058.

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Abstract Heart failure (HF) is a major and still growing medical problem and is characterized by episodes of acute decompensation that are associated with a negative prognosis and a significant burden on the patients, doctors, and healthcare resources. Early detection of incipient HF may allow outpatient treatment before patients severely decompensate, thus reducing HF hospitalizations and related costs. The HeartLogic™ algorithm is an automatic, remotely managed system combining data directly related to HF pathophysiology into a single score, the HeartLogic™ index. This index proved to be eff

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Li, Longhai, Ce Guo, Shun Xu, et al. "Mathematical Model and microCT-Based Kinematic Analysis of the Rostrum Mouthparts in Cyrtotrachelus buqueti Guer (Coleoptera: Curculionidae)." Microscopy and Microanalysis 27, no. 4 (2021): 860–77. http://dx.doi.org/10.1017/s143192762100043x.

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AbstractTo uncover the chewing mechanism of Cyrtotrachelus buqueti Guer, a mathematical model was created and a kinematic analysis of its rostrum mouthparts was conducted for, to our knowledge, the first time. To reduce noise and improve the quality of scanning electron micrographs of the weevil's mouthparts, nonlocal means and integral nonlocal means algorithms were proposed. Additionally, based on a comparison and analysis of five classical edge detection algorithms, a multiscale edge detection algorithm based on the B-spline wavelet was used to obtain the boundaries of structural features.

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Evans, W. S., A. C. Faria, E. Christiansen, et al. "Impact of intensive venous sampling on characterization of pulsatile GH release." American Journal of Physiology-Endocrinology and Metabolism 252, no. 4 (1987): E549—E556. http://dx.doi.org/10.1152/ajpendo.1987.252.4.e549.

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The effect of sampling intensity on quantitative properties of pulsatile growth hormone (GH) release was tested using an objective, statistically based pulse detection algorithm (Cluster). Seven normal young men had blood withdrawn at 5-min intervals for 24 h. The number of GH peaks/24 h (mean +/- SE) detected in the 5-min series (5.93 +/- 0.66) was significantly greater than the number of peaks documented in the constituent 10-min (3.45 +/- 0.28), 15-min (2.79 +/- 0.31), 20-min (2.86 +/- 0.64), 30-min (2.5 +/- 0.36), 45-min (2.21 +/- 0.21), and 60-min (1.93 +/- 0.23) series. The increased num

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Feijen, Michelle, Anastasia D. Egorova, Laurens F. Tops, et al. "The Potential of the HeartLogicTM Algorithm in Patients with a Left Ventricular Assist Device, an Initial Report." Journal of Cardiovascular Development and Disease 11, no. 2 (2024): 51. http://dx.doi.org/10.3390/jcdd11020051.

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Background: Survival and quality-of-life of left ventricular assist device (LVAD) recipients improved significantly because of growing experience and technological advances. However, LVAD-related complication rates, including recurrent episodes of congestion, remain high. Early detection of fluid retention to provide a time-window for medical intervention is the pillar in preventing hospitalizations. The multisensory HeartLogicTM algorithm accurately detected impending congestion in ambulant heart failure patients. The aim of the current study is to investigate the feasibility of HeartLogicTM-

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Minhad, Khairun Nisa’, Araf Farayez, Mamun Bin Ibne Reaz, Mohammad Arif Sobhan Bhuiyan, Siti Balqis Samdin, and Mahdi H. Miraz. "Early Diagnosis of Dementia Patients by SPADE Activity Prediction Algorithm." Measurement Science Review 23, no. 2 (2023): 86–91. http://dx.doi.org/10.2478/msr-2023-0011.

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Abstract Dementia is not a specific disease, but a general term for age-related decline or loss of memory, cognitive abilities including problem solving and decision-making, and one’s own language, which significantly interfere with daily life. Researchers around the world have developed ways to automate the diagnosis of dementia through the use of machine learning and data mining approaches. The aim of this research project is to design and develop a day-to-day activity prediction algorithm in order to accurately identify and differentiate the dementia affected patients from the healthy subje

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Nguyen, T. T., and Z. M. Yuldashev. "An Algorithm for Detection of Atrial Fibrillation Episodes and Generation of Alarm Signals Used in a Cardiac Rhythm Remote Monitoring System." Biomedical Engineering 52, no. 1 (2018): 51–55. http://dx.doi.org/10.1007/s10527-018-9780-5.

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