Here we present a method of QT interval meas-urement for Physionet's online QT Challenge ECG database using the combination of wavelet and time plane feature extraction mechanisms. For this we mainly combined two ...Here we present a method of QT interval meas-urement for Physionet's online QT Challenge ECG database using the combination of wavelet and time plane feature extraction mechanisms. For this we mainly combined two previous works one done using the Daubechies 6 wavelet and one time plane based with modifications in their algorithms and inclusion of two more wavelets (Daubechies 8 and Symlet 6). But found that out of these three wavelets Daube-chies 6 and 8 gives the best output and when averaged with the interval of time plane feature extraction method it gives least percentage of error with respect to the median reference QT interval as specified by Physionet. Our modified time plane feature extraction scheme along with the wavelet method together produces best re-sults for automated QT wave measurement as its regular verification is important for analyzing cardiac health. For the V2 chest lead particularly whose QT wave is of tremendous significance we have tested on 530 recordings of Physionet. This is because delay in cardiac repolarization causes ventricular tachyarrhythmias as well as Torsade de pointes (TdP). A feature of TdP is pronounced prolongation of the QT interval in the supraventricular beat preceding the ar-rhythmia. TdP can degenerate into ventricular fibrillation, leading to sudden death.展开更多
With the help of computer-aided diagnostic systems,cardiovascular diseases can be identified timely manner to minimize the mortality rate of patients suffering from cardiac disease.However,the early diagnosis of cardi...With the help of computer-aided diagnostic systems,cardiovascular diseases can be identified timely manner to minimize the mortality rate of patients suffering from cardiac disease.However,the early diagnosis of cardiac arrhythmia is one of the most challenging tasks.The manual analysis of electrocardiogram(ECG)data with the help of the Holter monitor is challenging.Currently,the Convolutional Neural Network(CNN)is receiving considerable attention from researchers for automatically identifying ECG signals.This paper proposes a 9-layer-based CNN model to classify the ECG signals into five primary categories according to the American National Standards Institute(ANSI)standards and the Association for the Advancement of Medical Instruments(AAMI).The Massachusetts Institute of Technology-Beth Israel Hospital(MIT-BIH)arrhythmia dataset is used for the experiment.The proposed model outperformed the previous model in terms of accuracy and achieved a sensitivity of 99.0%and a positivity predictively 99.2%in the detection of a Ventricular Ectopic Beat(VEB).Moreover,it also gained a sensitivity of 99.0%and positivity predictively of 99.2%for the detection of a supraventricular ectopic beat(SVEB).The overall accuracy of the proposed model is 99.68%.展开更多
Leveraging the power of artificial intelligence to facilitate an automatic analysis and monitoring of heart sounds has increasingly attracted tremendous efforts in the past decade.Nevertheless,lacking on standard open...Leveraging the power of artificial intelligence to facilitate an automatic analysis and monitoring of heart sounds has increasingly attracted tremendous efforts in the past decade.Nevertheless,lacking on standard open-access database made it difficult to maintain a sustainable and comparable research before the first release of the PhysioNet CinC Challenge Dataset.However,inconsistent standards on data collection,annotation,and partition are still restraining a fair and efficient comparison between different works.To this line,we introduced and benchmarked a first version of the Heart Sounds Shenzhen(HSS)corpus.Motivated and inspired by the previous works based on HSS,we redefined the tasks and make a comprehensive investigation on shallow and deep models in this study.First,we segmented the heart sound recording into shorter recordings(10 s),which makes it more similar to the human auscultation case.Second,we redefined the classification tasks.Besides using the 3 class categories(normal,moderate,and mild/severe)adopted in HSS,we added a binary classification task in this study,i.e.,normal and abnormal.In this work,we provided detailed benchmarks based on both the classic machine learning and the state-of-the-art deep learning technologies,which are reproducible by using open-source toolkits.Last but not least,we analyzed the feature contributions of best performance achieved by the benchmark to make the results more convincing and interpretable.展开更多
文摘Here we present a method of QT interval meas-urement for Physionet's online QT Challenge ECG database using the combination of wavelet and time plane feature extraction mechanisms. For this we mainly combined two previous works one done using the Daubechies 6 wavelet and one time plane based with modifications in their algorithms and inclusion of two more wavelets (Daubechies 8 and Symlet 6). But found that out of these three wavelets Daube-chies 6 and 8 gives the best output and when averaged with the interval of time plane feature extraction method it gives least percentage of error with respect to the median reference QT interval as specified by Physionet. Our modified time plane feature extraction scheme along with the wavelet method together produces best re-sults for automated QT wave measurement as its regular verification is important for analyzing cardiac health. For the V2 chest lead particularly whose QT wave is of tremendous significance we have tested on 530 recordings of Physionet. This is because delay in cardiac repolarization causes ventricular tachyarrhythmias as well as Torsade de pointes (TdP). A feature of TdP is pronounced prolongation of the QT interval in the supraventricular beat preceding the ar-rhythmia. TdP can degenerate into ventricular fibrillation, leading to sudden death.
基金supported by Faculty of Computing and Informatics,University Malaysia Sabah,Jalan UMS,Kota Kinabalu Sabah 88400,Malaysia.
文摘With the help of computer-aided diagnostic systems,cardiovascular diseases can be identified timely manner to minimize the mortality rate of patients suffering from cardiac disease.However,the early diagnosis of cardiac arrhythmia is one of the most challenging tasks.The manual analysis of electrocardiogram(ECG)data with the help of the Holter monitor is challenging.Currently,the Convolutional Neural Network(CNN)is receiving considerable attention from researchers for automatically identifying ECG signals.This paper proposes a 9-layer-based CNN model to classify the ECG signals into five primary categories according to the American National Standards Institute(ANSI)standards and the Association for the Advancement of Medical Instruments(AAMI).The Massachusetts Institute of Technology-Beth Israel Hospital(MIT-BIH)arrhythmia dataset is used for the experiment.The proposed model outperformed the previous model in terms of accuracy and achieved a sensitivity of 99.0%and a positivity predictively 99.2%in the detection of a Ventricular Ectopic Beat(VEB).Moreover,it also gained a sensitivity of 99.0%and positivity predictively of 99.2%for the detection of a supraventricular ectopic beat(SVEB).The overall accuracy of the proposed model is 99.68%.
基金partially supported by the Ministry of Science and Technology of the People's Republic of China with the STI2030-Major Projects(2021ZD0201900)the National Natural Science Foundation of China(No.62227807 and 62272044)+3 种基金the Teli Young Fellow Program from the Beijing Institute of Technology,Chinathe Natural Science Foundation of Shenzhen University General Hospital(No.SUGH2018QD013),Chinathe Shenzhen Science and Technology Innovation Commission Project(No.JCYJ20190808120613189),Chinathe Grants-in-Aid for Scientific Research(No.20H00569)from the Ministry of Education,Culture,Sports,Science and Technology(MEXT),Japan.
文摘Leveraging the power of artificial intelligence to facilitate an automatic analysis and monitoring of heart sounds has increasingly attracted tremendous efforts in the past decade.Nevertheless,lacking on standard open-access database made it difficult to maintain a sustainable and comparable research before the first release of the PhysioNet CinC Challenge Dataset.However,inconsistent standards on data collection,annotation,and partition are still restraining a fair and efficient comparison between different works.To this line,we introduced and benchmarked a first version of the Heart Sounds Shenzhen(HSS)corpus.Motivated and inspired by the previous works based on HSS,we redefined the tasks and make a comprehensive investigation on shallow and deep models in this study.First,we segmented the heart sound recording into shorter recordings(10 s),which makes it more similar to the human auscultation case.Second,we redefined the classification tasks.Besides using the 3 class categories(normal,moderate,and mild/severe)adopted in HSS,we added a binary classification task in this study,i.e.,normal and abnormal.In this work,we provided detailed benchmarks based on both the classic machine learning and the state-of-the-art deep learning technologies,which are reproducible by using open-source toolkits.Last but not least,we analyzed the feature contributions of best performance achieved by the benchmark to make the results more convincing and interpretable.
