Relevant bibliographies by topics / Morphological features of ECG

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
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Academic literature on the topic 'Morphological features of ECG'

Author: Grafiati
Published: 4 June 2021
Last updated: 16 February 2022

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Journal articles on the topic "Morphological features of ECG"

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Anwar, Syed Muhammad, Maheen Gul, Muhammad Majid, and Majdi Alnowami. "Arrhythmia Classification of ECG Signals Using Hybrid Features." Computational and Mathematical Methods in Medicine 2018 (November 12, 2018): 1–8. http://dx.doi.org/10.1155/2018/1380348.

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Automatic detection and classification of life-threatening arrhythmia plays an important part in dealing with various cardiac conditions. In this paper, a novel method for classification of various types of arrhythmia using morphological and dynamic features is presented. Discrete wavelet transform (DWT) is applied on each heart beat to obtain the morphological features. It provides better time and frequency resolution of the electrocardiogram (ECG) signal, which helps in decoding important information of a quasiperiodic ECG using variable window sizes. RR interval information is used as a dynamic feature. The nonlinear dynamics of RR interval are captured using Teager energy operator, which improves the arrhythmia classification. Moreover, to remove redundancy, DWT subbands are subjected to dimensionality reduction using independent component analysis, and a total of twelve coefficients are selected as morphological features. These hybrid features are combined and fed to a neural network to classify arrhythmia. The proposed algorithm has been tested over MIT-BIH arrhythmia database using 13724 beats and MIT-BIH supraventricular arrhythmia database using 22151 beats. The proposed methodology resulted in an improved average accuracy of 99.75% and 99.84% for class- and subject-oriented scheme, respectively, using three-fold cross validation.
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Lu, Peng, Hao Xi, Bing Zhou, Hongpo Zhang, Yusong Lin, Liwei Chen, Yang Gao, Yabin Zhang, and Yanhua Hu. "A New Multichannel Parallel Network Framework for the Special Structure of Multilead ECG." Journal of Healthcare Engineering 2020 (December 3, 2020): 1–15. http://dx.doi.org/10.1155/2020/8889483.

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Electrocardiogram (ECG) contains the rhythmic features of continuous heartbeat and morphological features of ECG waveforms and varies among different diseases. Based on ECG signal features, we propose a combination of multiple neural networks, the multichannel parallel neural network (MLCNN-BiLSTM), to explore feature information contained in ECG. The MLCNN channel is used in extracting the morphological features of ECG waveforms. Compared with traditional convolutional neural network (CNN), the MLCNN can accurately extract strong relevant information on multilead ECG while ignoring irrelevant information. It is suitable for the special structures of multilead ECG. The Bidirectional Long Short-Term Memory (BiLSTM) channel is used in extracting the rhythmic features of ECG continuous heartbeat. Finally, by initializing the core threshold parameters and using the backpropagation algorithm to update automatically, the weighted fusion of the temporal-spatial features extracted from multiple channels in parallel is used in exploring the sensitivity of different cardiovascular diseases to morphological and rhythmic features. Experimental results show that the accuracy rate of multiple cardiovascular diseases is 87.81%, sensitivity is 86.00%, and specificity is 87.76%. We proposed the MLCNN-BiLSTM neural network that can be used as the first-round screening tool for clinical diagnosis of ECG.
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Kamath, Chandrakar. "ECG classification using morphological features derived from symbolic dynamics." International Journal of Biomedical Engineering and Technology 9, no. 4 (2012): 325. http://dx.doi.org/10.1504/ijbet.2012.049217.

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Can Ye, B. V. K. Vijaya Kumar, and M. T. Coimbra. "Heartbeat Classification Using Morphological and Dynamic Features of ECG Signals." IEEE Transactions on Biomedical Engineering 59, no. 10 (October 2012): 2930–41. http://dx.doi.org/10.1109/tbme.2012.2213253.

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Rodrigues, João, David Belo, and Hugo Gamboa. "Noise detection on ECG based on agglomerative clustering of morphological features." Computers in Biology and Medicine 87 (August 2017): 322–34. http://dx.doi.org/10.1016/j.compbiomed.2017.06.009.

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Choi, Gyu Ho, Hoon Ko, Witold Pedrycz, Amit Kumar Singh, and Sung Bum Pan. "Recognition System Using Fusion Normalization Based on Morphological Features of Post-Exercise ECG for Intelligent Biometrics." Sensors 20, no. 24 (December 12, 2020): 7130. http://dx.doi.org/10.3390/s20247130.

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Although biometrics systems using an electrocardiogram (ECG) have been actively researched, there is a characteristic that the morphological features of the ECG signal are measured differently depending on the measurement environment. In general, post-exercise ECG is not matched with the morphological features of the pre-exercise ECG because of the temporary tachycardia. This can degrade the user recognition performance. Although normalization studies have been conducted to match the post- and pre-exercise ECG, limitations related to the distortion of the P wave, QRS complexes, and T wave, which are morphological features, often arise. In this paper, we propose a method for matching pre- and post-exercise ECG cycles based on time and frequency fusion normalization in consideration of morphological features and classifying users with high performance by an optimized system. One cycle of post-exercise ECG is expanded by linear interpolation and filtered with an optimized frequency through the fusion normalization method. The fusion normalization method aims to match one post-exercise ECG cycle to one pre-exercise ECG cycle. The experimental results show that the average similarity between the pre- and post-exercise states improves by 25.6% after normalization, for 30 ECG cycles. Additionally, the normalization algorithm improves the maximum user recognition performance from 96.4 to 98%.
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Yücelbaş, Şule, Cüneyt Yücelbaş, Gülay Tezel, Seral Özşen, Serkan Küççüktürk, and Şebnem Yosunkaya. "Pre-determination of OSA degree using morphological features of the ECG signal." Expert Systems with Applications 81 (September 2017): 79–87. http://dx.doi.org/10.1016/j.eswa.2017.03.049.

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DAS, MANAB KUMAR, and SAMIT ARI. "ELECTROCARDIOGRAM BEAT CLASSIFICATION USING S-TRANSFORM BASED FEATURE SET." Journal of Mechanics in Medicine and Biology 14, no. 05 (August 2014): 1450066. http://dx.doi.org/10.1142/s0219519414500663.

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In this paper, the conventional Stockwell transform is effectively used to classify the ECG arrhythmias. The performance of ECG classification mainly depends on feature extraction based on an efficient formation of morphological and temporal features and the design of the classifier. Feature extraction is the important component of designing the system based on pattern recognition since even the best classifier will not perform better if the good features are not selected properly. Here, the S-transform (ST) is used to extract the morphological features which is appended with temporal features. This feature set is independently classified using artificial neural network (NN) and support vector machine (SVM). In this work, five classes of ECG beats (normal, ventricular, supra ventricular, fusion and unknown beats) from Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database are classified according to AAMI EC57 1998 standard (Association for the Advancement of Medical Instrumentation). Performance is evaluated on several normal and abnormal ECG signals of MIT-BIH arrhythmias database using two classifier techniques: ST with NN classifier (ST-NN) and other proposed ST with SVM classifier (ST-SVM). The proposed method achieves accuracy of 98.47%. The performance of the proposed technique is compared with ST-NN and earlier reported technique.
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Liang, Yongbo, Zhencheng Chen, Rabab Ward, and Mohamed Elgendi. "Hypertension Assessment via ECG and PPG Signals: An Evaluation Using MIMIC Database." Diagnostics 8, no. 3 (September 10, 2018): 65. http://dx.doi.org/10.3390/diagnostics8030065.

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Cardiovascular diseases (CVDs) have become the biggest threat to human health, and they are accelerated by hypertension. The best way to avoid the many complications of CVDs is to manage and prevent hypertension at an early stage. However, there are no symptoms at all for most types of hypertension, especially for prehypertension. The awareness and control rates of hypertension are extremely low. In this study, a novel hypertension management method based on arterial wave propagation theory and photoplethysmography (PPG) morphological theory was researched to explore the physiological changes in different blood pressure (BP) levels. Pulse Arrival Time (PAT) and photoplethysmogram (PPG) features were extracted from electrocardiogram (ECG) and PPG signals to represent the arterial wave propagation theory and PPG morphological theory, respectively. Three feature sets, one containing PAT only, one containing PPG features only, and one containing both PAT and PPG features, were used to classify the different BP categories, defined as normotension, prehypertension, and hypertension. PPG features were shown to classify BP categories more accurately than PAT. Furthermore, PAT and PPG combined features improved the BP classification performance. The F1 scores to classify normotension versus prehypertension reached 84.34%, the scores for normotension versus hypertension reached 94.84%, and the scores for normotension plus prehypertension versus hypertension reached 88.49%. This indicates that the simultaneous collection of ECG and PPG signals could detect hypertension.
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Belova, G. V., O. S. Rudenko, T. A. Karmakova, M. E. Yudakova, and V. S. Sidorenko. "Barrett’s esophagus: genetic features." Experimental and Clinical Gastroenterology 174, no. 5 (June 29, 2020): 36–41. http://dx.doi.org/10.31146/1682-8658-ecg-177-5-36-41.

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The aim of the study was to evaluate the genetic characteristics of the pathological process in patients with biopsy-proven PB. Materials and methods: Dynamic observation and treatment of patients with morphologically confi rmed diagnosis of Barrett’s esophagus (PB) was performed at the Bank of Russia Multidisciplinary medical center in Moscow in the period from 2014 to 2019.the study included 52 patients. Among them, men — 33 (63.4%) and women –19 (36.5%) aged from 28 to 70 years, the peak incidence was mainly in the age range from 50–71 years. As a result, morphological types of PB epithelium were identifi ed at the time of the study: cardiac — 8(15.3%), small — bowel — 25 (48%), large-bowel without dysplasia-17 (32.6%) and large-bowel with mild dysplasia foci –2 (3.8%). The expression of the p53, P63, and Ki-67 genes responsible for cell proliferation and diff erentiation was studied using an immunohistochemical method. Results. As a result of the study, it was found that genetic changes in patients with morphologically confi rmed PB increase from the cardiac type of epithelium in the segment to the detection of dysplasia foci on the background of colonic metaplasia. An exception is tokokishechnaya metaplasia, which may be an indirect sign of the development of highly diff erentiated epithelium in response to gastroesophageal or duodenogastroesophageal refl ux Summary. 1. Mutation of the p 53 genes responsible for regulating cell transcription and activating apoptosis and the P63 gene responsible for cell diff erentiation occurs in all morphological types of PB, with the number of mutations increasing as metastatic changes progress in accordance with the Correa cascade 2. The combination of p53 and P63 gene mutations in almost equal proportions indicates parallel processes of cell proliferation and diff erentiation disorders. 3. Ki-67 expression increases from the cardiac type of epithelium in the segment to the detection of dysplasia foci on the background of metaplastic epithelium. An exception is tokokishechnaya metaplasia, which may be an indirect sign of the development of highly diff erentiated epithelium in response to gastroesophageal or duodenogastroesophageal refl ux. Conclusion. Studies have shown that there are a number of genetic features of the pathological process in patients with morphologically confi rmed PB, the combination of which is more reliable and reliable assessment of the risk of neoplastic changes than individual indicators. To date, it remains relevant to develop a marker panel that is suitable for use in clinical practice, informative for both assessing individual risk and stratifying risk groups, and useful for monitoring the eff ectiveness of treatment.
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Dissertations / Theses on the topic "Morphological features of ECG"

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Lei, Wai Kei. "An exploration of intelligent ECG interpretation using morphological feature characterization and support vector machine classification." Thesis, University of Macau, 2007. http://umaclib3.umac.mo/record=b1692866.

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Gawde, Purva R. "INTEGRATED ANALYSIS OF TEMPORAL AND MORPHOLOGICAL FEATURES USING MACHINE LEARNING TECHNIQUES FOR REAL TIME DIAGNOSIS OF ARRHYTHMIA AND IRREGULAR BEATS." Kent State University / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=kent1544106866041632.

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Tichý, Pavel. "Detekce ischemie v EKG záznamech." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2014. http://www.nusl.cz/ntk/nusl-220839.

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This paper describes the manifestations of ischemia in the ECG signals and summarizes some methods allowing automatic detection of ischemia. Morphological features were then calculated from ECG signals available from UBMI and statistically evaluated to select features appropriate for further automatic classification. Multilayer feedforward neural network was used for classification of heart beats. The neural network was designed in Matlab. Classification performance up to 99.9% was obtained on available dataset.
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Newell, John T. "Pixel classification by morphological granulometric features /." Online version of thesis, 1991. http://hdl.handle.net/1850/11210.

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Noyer, Robert Rolf. "Features, positions and affixes in autonomous morphological structure." Thesis, Massachusetts Institute of Technology, 1992. http://hdl.handle.net/1721.1/12895.

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Bozed, Kenz Amhmed. "Detection of facial expressions based on time dependent morphological features." Thesis, University of Bedfordshire, 2011. http://hdl.handle.net/10547/145618.

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Facial expression detection by a machine is a valuable topic for Human Computer Interaction and has been a study issue in the behavioural science for some time. Recently, significant progress has been achieved in machine analysis of facial expressions but there are still some interestes to study the area in order to extend its applications. This work investigates the theoretical concepts behind facial expressions and leads to the proposal of new algorithms in face detection and facial feature localisation, design and construction of a prototype system to test these algorithms. The overall goals and motivation of this work is to introduce vision based techniques able to detect and recognise the facial expressions. In this context, a facial expression prototype system is developed that accomplishes facial segmentation (i.e. face detection, facial features localisation), facial features extraction and features classification. To detect a face, a new simplified algorithm is developed to detect and locate its presence from the fackground by exploiting skin colour properties which are then used to distinguish between face and non-face regions. This allows facial parts to be extracted from a face using elliptical and box regions whose geometrical relationships are then utilised to determine the positions of the eyes and mouth through morphological operations. The mean and standard deviations of segmented facial parts are then computed and used as features for the face. For images belonging to the same class, thses features are applied to the K-mean algorithm to compute the controid point of each class expression. This is repeated for images in the same expression class. The Euclidean distance is computed between each feature point and its cluster centre in the same expression class. This determines how close a facial expression is to a particular class and can be used as observation vectors for a Hidden Markov Model (HMM) classifier. Thus, an HMM is built to evaluate an expression of a subject as belonging to one of the six expression classes, which are Joy, Anger, Surprise, Sadness, Fear and Disgust by an HMM using distance features. To evaluate the proposed classifier, experiments are conducted on new subjects using 100 video clips that contained a mixture of expressions. The average successful detection rate of 95.6% is measured from a total of 9142 frames contained in the video clips. The proposed prototype system processes facial features parts and presents improved results of facial expressions detection rather than using whole facial features as proposed by previous authors. This work has resulted in four contributions: the Ellipse Box Face Detection Algorithm (EBFDA), Facial Features Distance Algorithm (FFDA), Facial features extraction process, and Facial features classification. These were tested and verified using the prototype system.
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Han, Richard I.-Ming. "Morphological, cellular and proteomic features of canine myxomatous mitral valve disease." Thesis, University of Edinburgh, 2009. http://hdl.handle.net/1842/4286.

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Myxomatous mitral valve degeneration (MMVD) is the single most common cardiac disease of the dog, and is analogous to Mitral Valve Prolapse in humans. Very little is known about the aetiopathogenesis of this disease or the changes in valvular interstitial cell populations in diseased valves. The aim of this study was to identify morphological, cellular and molecular changes associated with MMVD. Mitral valve leaflets from both normal and varying grades (Whitney’s 1-4) of diseased dogs were subject to image analysis, immunophenotyping, proteomics and RT-PCR. Image analysis - leaflet thickening due to accumulation of glycosaminoglycan was significant in this disease. MMVD is associated with loss of connective tissue, reduction in cell numbers but no change in cell shape in the overtly myxomatous area. Near the surface, increase in valvular interstitial cells (VIC) towards the damaged endothelium in concert with destruction of collagen and building up of ground substance was manifested during the disease process. Immunophenotyping - activated myofibroblasts were increased and fibroblast-like VICs were reduced without any change in desmin and myosin expression in MMVD compared to clinical normal dogs. In addition, other cell types like macrophage, adipocyte, chondrocyte, mast cell, and stem cell were identified and their possible role in MMVD is discussed. Proteomics - a protein expression profile was established, with 64 proteins being positively identified from dog’s mitral valve using 1-D SDS PAGE LC/MS. Amongst them 44 proteins were differentially expressed comparing normal and severely diseased. Two actin binding proteins, tropomyosin alpha and myosin light chain-2 were found to be differentially expressed in the normal but down regulated in the diseased. RT-PCR was used to assess the expression of 8 genes of interest. Their expression was compared with 3 different housekeeping genes.
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De, Klerk Willouw. "A study of morphological, immunohistochemical and histochemical features of ampullary carcinomas." Master's thesis, University of Cape Town, 2005. http://hdl.handle.net/11427/3075.

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Includes bibliographical references (leaves 66-75).
The aim of the first study was to examine clinical, histopathological and immunohistochemical features of ampullary carcinomas and to determine whether any of these features had significant prognostiC value. The immunohistochemical panel was selected after a literature review and included p53, Ki-67, MUC1, MUC1core, MUC2 and CA 19.9. The data was analyzed by multivariate analysis.
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Di, Fusco Greta. "A Reliable Downscaling of ECG Signals for the Detection of T wave Heterogeneity Features." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2016.

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In cardiovascular disease the definition and the detection of the ECG parameters related to repolarization dynamics in post MI patients is still a crucial unmet need. In addition, the use of a 3D sensor in the implantable medical devices would be a crucial mean in the assessment or prediction of Heart Failure status, but the inclusion of such feature is limited by hardware and firmware constraints. The aim of this thesis is the definition of a reliable surrogate of the 500 Hz ECG signal to reach the aforementioned objective. To evaluate the worsening of reliability due to sampling frequency reduction on delineation performance, the signals have been consecutively down sampled by a factor 2, 4, 8 thus obtaining the ECG signals sampled at 250, 125 and 62.5 Hz, respectively. The final goal is the feasibility assessment of the detection of the fiducial points in order to translate those parameters into meaningful clinical parameter for Heart Failure prediction, such as T waves intervals heterogeneity and variability of areas under T waves. An experimental setting for data collection on healthy volunteers has been set up at the Bakken Research Center in Maastricht. A 16 – channel ambulatory system, provided by TMSI, has recorded the standard 12 – Leads ECG, two 3D accelerometers and a respiration sensor. The collection platform has been set up by the TMSI property software Polybench, the data analysis of such signals has been performed with Matlab. The main results of this study show that the 125 Hz sampling rate has demonstrated to be a good candidate for a reliable detection of fiducial points. T wave intervals proved to be consistently stable, even at 62.5 Hz. Further studies would be needed to provide a better comparison between sampling at 250 Hz and 125 Hz for areas under the T waves.
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Abu, Dalou Ahmad Y. "The validity of morphological features and osteological markers in reconstructing habitual activities." Diss., Columbia, Mo. : University of Missouri-Columbia, 2007. http://hdl.handle.net/10355/4877.

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Thesis (Ph.D.)--University of Missouri-Columbia, 2007.
The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file (July 17, 2008) Includes bibliographical references.
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Books on the topic "Morphological features of ECG"

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Noyer, Rolf. Features, positions, and affixes in autonomous morphological structure. New York: Garland Pub., 1997.

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Žd̕árská, Zdeňka. Evolution of morphological features in larval stages of digenetic trematodes. Praha: Academia, nakl. Československé akademie věd, 1986.

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Aroca, R. Plant responses to drought stress: From morphological to molecular features. Heidelberg: Springer, 2012.

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Prendeville, Tadgh. A study of the morphological features of pores occurring in bone cement. Dublin: University College Dublin, 1996.

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Maher, Suzanne A. Modelling the morphological features of the cement/bone interface in hip replacements. Dublin: University College Dublin, 1996.

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Miller, Orson K. Gasteromycetes: Morphological and developmental features, with keys to the orders, families, and genera. Eureka, CA: Mad River Press, 1988.

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Ramos, Carlos. Quantification of stream channel morphological features: Recommended procedures for use in watershed analysis and TFW ambient monitoring. [Olympia, Wash.?: The Commission?, 1996.

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Borg, Erik. Noise-induced hearing loss: Literature review and experiments in rabbits : morphological and electrophysiological features, exposure parameters and temporal factors, variability and interactions. Oslo: Scandinavian University Press, 1995.

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Cường, Nguyẽ̂n Lân. Nghiên cứu vè̂ đặc điẻ̂m hình thái, chủng tộc và bệnh lý răng người cỏ̂ thuộc thời đại kim khí ở miè̂n Bá̆c Việt Nam =: Study on morphological, racial and pathological features of the dentition of ancient people in the Northern Vietnam metal age. Hà Nội: Nhà xuá̂t bản Khoa học xã hội, 2003.

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Uskov, Aleksandr, Evgeniy Mozhaev, Lyudmila Uskova, and Elena Zakabunina. Potato growing. ru: INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1030568.

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The textbook covers the main topics related to the national economic significance, origin, distribution of potatoes; morphological and anatomical structure of potato plants. Features of potato biology by periods of growth and development, as well as its requirements for growing conditions are given. Technological methods of cultivation, the system of fertilization and protection from pests, diseases and weeds, seed production and varietal studies, the economy of potato production are presented. Meets the requirements of the Federal state educational standards of higher education of the latest generation for the preparation of bachelors. For undergraduate students studying in the field of "agronomy", as well as specialists in agricultural production.
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Book chapters on the topic "Morphological features of ECG"

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Carreiras, Carlos, André Lourenço, Helena Aidos, Hugo Plácido da Silva, and Ana L. N. Fred. "Unsupervised Analysis of Morphological ECG Features for Attention Detection." In Studies in Computational Intelligence, 437–53. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23392-5_24.

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El-Saadawy, Hadeer, Manal Tantawi, Howida A. Shedeed, and M. F. Tolba. "Diagnosing Heart Diseases Using Morphological and Dynamic Features of Electrocardiogram (ECG)." In Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2017, 342–52. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-64861-3_32.

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Murugappan, M., Hui Boon, Alex Noel Joseph Raj, Gokul Krishnan, and Karthikeyan Palanisamy. "ECG Morphological Features Based Sudden Cardiac Arrest (SCA) Prediction Using Nonlinear Classifiers." In Lecture Notes in Electrical Engineering, 789–801. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5558-9_68.

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Arenas, Wilson J., Silvia A. Sotelo, Martha L. Zequera, and Miguel Altuve. "Morphological and Temporal ECG Features for Myocardial Infarction Detection Using Support Vector Machines." In IFMBE Proceedings, 172–81. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30648-9_24.

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Laudato, Gennaro, Franco Boldi, Angela Rita Colavita, Giovanni Rosa, Simone Scalabrino, Aldo Lazich, and Rocco Oliveto. "Combining Rhythmic and Morphological ECG Features for Automatic Detection of Atrial Fibrillation: Local and Global Prediction Models." In Biomedical Engineering Systems and Technologies, 425–41. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72379-8_21.

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Weng, Julia Tzu-Ya, Jyun-Jie Lin, Yi-Cheng Chen, and Pei-Chann Chang. "Myocardial Infarction Classification by Morphological Feature Extraction from Big 12-Lead ECG Data." In Lecture Notes in Computer Science, 689–99. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-13186-3_61.

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Sharma, N., R. Chakrabarty, and C. S. P. Ojha. "Spatio-Temporal Morphological Features." In The Brahmaputra Basin Water Resources, 214–28. Dordrecht: Springer Netherlands, 2004. http://dx.doi.org/10.1007/978-94-017-0540-0_10.

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Detweiler, D. K. "The Mammalian Electrocardiogram: Comparative Features." In Specialized Aspects of ECG, 491–529. London: Springer London, 2011. http://dx.doi.org/10.1007/978-0-85729-880-5_10.

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Behuet, Sabrina, Sebastian Bludau, Olga Kedo, Christian Schiffer, Timo Dickscheid, Andrea Brandstetter, Philippe Massicotte, Mona Omidyeganeh, Alan Evans, and Katrin Amunts. "A High-Resolution Model of the Human Entorhinal Cortex in the ‘BigBrain’ – Use Case for Machine Learning and 3D Analyses." In Lecture Notes in Computer Science, 3–21. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-82427-3_1.

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AbstractThe ‘BigBrain’ is a high-resolution data set of the human brain that enables three-dimensional (3D) analyses with a 20 µm spatial resolution at nearly cellular level. We use this data set to explore pre-α (cell) islands of layer 2 in the entorhinal cortex (EC), which are early affected in Alzheimer’s disease and have therefore been the focus of research for many years. They appear mostly in a round and elongated shape as shown in microscopic studies. Some studies suggested that islands may be interconnected based on analyses of their shape and size in two-dimensional (2D) space. Here, we characterized morphological features (shape, size, and distribution) of pre-α islands in the ‘BigBrain’, based on 3D-reconstructions of gapless series of cell-body-stained sections. The EC was annotated manually, and a machine-learning tool was trained to identify and segment islands with subsequent visualization using high-performance computing (HPC). Islands were visualized as 3D surfaces and their geometry was analyzed. Their morphology was complex: they appeared to be composed of interconnected islands of different types found in 2D histological sections of EC, with various shapes in 3D. Differences in the rostral-to-caudal part of EC were identified by specific distribution and size of islands, with implications for connectivity and function of the EC. 3D compactness analysis found more round and complex islands than elongated ones. The present study represents a use case for studying large microscopic data sets. It provides reference data for studies, e.g. investigating neurodegenerative diseases, where specific alterations in layer 2 were previously reported.
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Li, Shengyang, Xiaoyun Zhang, Xiaoxia Wang, Yumin Zhong, Xiaofen Yao, Ya Zhang, and Yanfeng Wang. "Children’s Neuroblastoma Segmentation Using Morphological Features." In Machine Learning in Medical Imaging, 81–88. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32692-0_10.

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Conference papers on the topic "Morphological features of ECG"

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Riasi, Atiye, and Maryam Mohebbi. "Prediction of ventricular tachycardia using morphological features of ECG signal." In 2015 International Symposium on Artificial Intelligence and Signal Processing (AISP). IEEE, 2015. http://dx.doi.org/10.1109/aisp.2015.7123515.

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Jekova, I., I. Christov, V. Krasteva, G. Bortolan, and M. Matveev. "Assessment of the potential of morphological ECG features for person identification." In 2015 Computing in Cardiology Conference (CinC). IEEE, 2015. http://dx.doi.org/10.1109/cic.2015.7411062.

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Alim, Anika, and Md Kafiul Islam. "Application of Machine Learning on ECG Signal Classification Using Morphological Features." In 2020 IEEE Region 10 Symposium (TENSYMP). IEEE, 2020. http://dx.doi.org/10.1109/tensymp50017.2020.9230780.

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Maier, C., H. Dickhaus, M. Bauch, and T. Penzel. "Comparison of heart rhythm and morphological ECG features in recognition of sleep apnea from the ECG." In Computers in Cardiology, 2003. IEEE, 2003. http://dx.doi.org/10.1109/cic.2003.1291153.

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Can Ye, Miguel Tavares Coimbra, and B. V. K. Vijaya Kumar. "Arrhythmia detection and classification using morphological and dynamic features of ECG signals." In 2010 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2010). IEEE, 2010. http://dx.doi.org/10.1109/iembs.2010.5627645.

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Laudato, Gennaro, Franco Boldi, Angela Colavita, Giovanni Rosa, Simone Scalabrino, Paolo Torchitti, Aldo Lazich, and Rocco Oliveto. "Combining Rhythmic and Morphological ECG Features for Automatic Detection of Atrial Fibrillation." In 13th International Conference on Health Informatics. SCITEPRESS - Science and Technology Publications, 2020. http://dx.doi.org/10.5220/0008982301560165.

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Yang, Hui, and Zhiqiang Wei. "Discrimination of Cardiac Arrhythmia using a Fusion Classifier with ECG Morphological Features." In ICBBS 2019: 2019 8th International Conference on Bioinformatics and Biomedical Science. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3369166.3369167.

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Ghongade, Rajesh, and Ashok Ghatol. "A robust and reliable ECG pattern classification using QRS morphological features and ANN." In TENCON 2008 - 2008 IEEE Region 10 Conference (TENCON). IEEE, 2008. http://dx.doi.org/10.1109/tencon.2008.4766722.

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Kutlu, Yakup, Mehmet Kuntalp, and Damla Kuntalp. "Topographic analysis of morphologic features of ECG beats." In 2009 14th National Biomedical Engineering Meeting. IEEE, 2009. http://dx.doi.org/10.1109/biyomut.2009.5130289.

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Laurino, Marco, Andrea Piarulli, Remo Bedini, Angelo Gemignani, Alessandro Pingitore, Antonio L'Abbate, Alberto Landi, Paolo Piaggi, and Danilo Menicucci. "Comparative study of morphological ECG features classificators: An application on athletes undergone to acute physical stress." In 2011 11th International Conference on Intelligent Systems Design and Applications (ISDA). IEEE, 2011. http://dx.doi.org/10.1109/isda.2011.6121662.

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Reports on the topic "Morphological features of ECG"

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Brodie, Katherine, Ian Conery, Nicholas Cohn, Nicholas Spore, and Margaret Palmsten. Spatial variability of coastal foredune evolution, part A : timescales of months to years. Engineer Research and Development Center (U.S.), July 2021. http://dx.doi.org/10.21079/11681/41322.

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Abstract:
Coastal foredunes are topographically high features that can reduce vulnerability to storm-related flooding hazards. While the dominant aeolian, hydrodynamic, and ecological processes leading to dune growth and erosion are fairly well-understood, predictive capabilities of spatial variations in dune evolution on management and engineering timescales (days to years) remain relatively poor. In this work, monthly high-resolution terrestrial lidar scans were used to quantify topographic and vegetation changes over a 2.5 year period along a micro-tidal intermediate beach and dune. Three-dimensional topographic changes to the coastal landscape were used to investigate the relative importance of environmental, ecological, and morphological factors in controlling spatial and temporal variability in foredune growth patterns at two 50 m alongshore stretches of coast. Despite being separated by only 700 m in the alongshore, the two sites evolved differently over the study period. The northern dune retreated landward and lost volume, whereas the southern dune prograded and vertically accreted. The largest differences in dune response between the two sections of dunes occurred during the fall storm season, when each of the systems’ geomorphic and ecological properties modulated dune growth patterns. These findings highlight the complex eco-morphodynamic feedback controlling dune dynamics across a range of spatial scales.
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Rossi, John, Messier III., Heyder Arthur A., and Elly. Morphological Features Accompanying Neural Differentiation of Neuroblastoma (N-2A) Induced by Media Serum Availability. Fort Belvoir, VA: Defense Technical Information Center, July 1989. http://dx.doi.org/10.21236/ada418643.

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