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OMAR M O H A M M E D A M I N ALI

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Biometric Systems: A Comprehensive Review by OMAR M O H A M M E D A M I N ALI

Research paper thumbnail of Comparative Evaluation for Two and Five Classes ECG Signal Classification: Applied Deep Learning

ECG can be using to reliably monitor the health of a cardiovascular system. The proper classifica... more ECG can be using to reliably monitor the health of a cardiovascular system. The proper classification of heartbeats has received a lot of attention recently. While there are numerous similarities among ECG situations, instead of learning and to use transferable knowledge across tasks, most research has concentrated on classify a group of situations using a dataset labeled for that task. This research suggested a technique for heartbeat classification based using deep CNN models that can accurately categorize distinct arrhythmias for two and five classes ECG signals in line to AAMI EC57 standards. The authors also propose a strategy for transferring this job's knowledge to the myocardial infarction (MI) categorization problem. Physician Net's MIT-BIH and PTB Diagnostic databases were used to evaluate the suggested technique. The ECG data was gathered from various Physio Bank databases, which provide clinical research data freely available. Images of ECG signals with time-frequency encoding were fed into architecture such as CNN, LSTM, Alex Net, VGG-16, Resnet50, and Inception. The categorization of ECGs was completed, as well as the performance of CNN, LSTM, Alex Net, VGG-16, Resnet50, and Inception architectures were evaluated using a transfer learning technique and modifications in particular output layers for five designs.

Conference Presentations by OMAR M O H A M M E D A M I N ALI

Research paper thumbnail of Evaluation of electrocardiogram signals classification using CNN, SVM, and LSTM algorithm: A review

IEEE, 2022

The non-stationary signals of Electrocardiogram (ECG) are widely utilized to assess heartbeat rat... more The non-stationary signals of Electrocardiogram (ECG) are widely utilized to assess heartbeat rate and tune the major goal of this study is to give an overview of ECG classification Machine learning and neural network methods are employed. Furthermore, the major stage in ECG classification is feature extraction, which is used to identify a group of important characteristics that may achieve the highest level of accuracy. The optimization approach is used in conjunction with classifiers to get the optimal value for Its discriminant purpose was best served by using classifying parameters that best fit the discriminant purpose. Finally, this study evaluates the signal classification for ECG heartbeat using a Convolution Neural Network (CNN), Support Vector Machine (SVM), and Long Short Term Memory (LSTM), compare between them and present that the best method is LSTM for these cases based on the dataset.

Research paper thumbnail of Comparative Evaluation for Two and Five Classes ECG Signal Classification: Applied Deep Learning

ECG can be using to reliably monitor the health of a cardiovascular system. The proper classifica... more ECG can be using to reliably monitor the health of a cardiovascular system. The proper classification of heartbeats has received a lot of attention recently. While there are numerous similarities among ECG situations, instead of learning and to use transferable knowledge across tasks, most research has concentrated on classify a group of situations using a dataset labeled for that task. This research suggested a technique for heartbeat classification based using deep CNN models that can accurately categorize distinct arrhythmias for two and five classes ECG signals in line to AAMI EC57 standards. The authors also propose a strategy for transferring this job's knowledge to the myocardial infarction (MI) categorization problem. Physician Net's MIT-BIH and PTB Diagnostic databases were used to evaluate the suggested technique. The ECG data was gathered from various Physio Bank databases, which provide clinical research data freely available. Images of ECG signals with time-frequency encoding were fed into architecture such as CNN, LSTM, Alex Net, VGG-16, Resnet50, and Inception. The categorization of ECGs was completed, as well as the performance of CNN, LSTM, Alex Net, VGG-16, Resnet50, and Inception architectures were evaluated using a transfer learning technique and modifications in particular output layers for five designs.

Research paper thumbnail of Evaluation of electrocardiogram signals classification using CNN, SVM, and LSTM algorithm: A review

IEEE, 2022

The non-stationary signals of Electrocardiogram (ECG) are widely utilized to assess heartbeat rat... more The non-stationary signals of Electrocardiogram (ECG) are widely utilized to assess heartbeat rate and tune the major goal of this study is to give an overview of ECG classification Machine learning and neural network methods are employed. Furthermore, the major stage in ECG classification is feature extraction, which is used to identify a group of important characteristics that may achieve the highest level of accuracy. The optimization approach is used in conjunction with classifiers to get the optimal value for Its discriminant purpose was best served by using classifying parameters that best fit the discriminant purpose. Finally, this study evaluates the signal classification for ECG heartbeat using a Convolution Neural Network (CNN), Support Vector Machine (SVM), and Long Short Term Memory (LSTM), compare between them and present that the best method is LSTM for these cases based on the dataset.

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