Juan Pablo Moreno, Miguel A. Sepúlveda, Esteban J. Pino
{"title":"一维卷积神经网络对心电图和 BCG 信号心率指标的影响","authors":"Juan Pablo Moreno, Miguel A. Sepúlveda, Esteban J. Pino","doi":"10.1007/s40846-024-00872-w","DOIUrl":null,"url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Purpose</h3><p>The presence of motion artifacts (MA) in cardiac signals negatively impacts the reliability of higher-level information such as the Heart Rate (HR), and therefore the correct diagnosis of pathologies. This paper proposes an MA detection method, based on One-Dimensional Convolutional Neural Networks (1D CNN), to label noisy zones of signals as unreliable, and subsequently avoid them for metric calculations.</p><h3 data-test=\"abstract-sub-heading\">Methods</h3><p>To validate the concept, we first design a CNN to detect MAs in electrocardiogram (ECG) recordings from MIT–BIH Arrhythmia and Noise Stress Test Databases. This network extracts features from 1 s data segments, and then classifies them as clean or noisy. Also, we then train a tuned version of the model with semi-synthetic ballistocardiogram (BCG) signals.</p><h3 data-test=\"abstract-sub-heading\">Results</h3><p>The classification in ECG achieves an accuracy of 95.9% and the BCG classification obtains an accuracy of 91.1%. Both classifiers are incorporated into beat detection systems, which produce an increase in the sensitivity of the detection algorithms from 75 to 98.5% in the ECG case, and from 72.1 to 94.5% in the case of BCG, for signals contaminated at 0 dB of SNR.</p><h3 data-test=\"abstract-sub-heading\">Conclusion</h3><p>We propose that this method will improve accuracy of any processing algorithm on BCG signals by identifying useful segments where a high accuracy can be achieved.</p>","PeriodicalId":50133,"journal":{"name":"Journal of Medical and Biological Engineering","volume":null,"pages":null},"PeriodicalIF":1.6000,"publicationDate":"2024-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"1D Convolutional Neural Network Impact on Heart Rate Metrics for ECG and BCG Signals\",\"authors\":\"Juan Pablo Moreno, Miguel A. Sepúlveda, Esteban J. Pino\",\"doi\":\"10.1007/s40846-024-00872-w\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<h3 data-test=\\\"abstract-sub-heading\\\">Purpose</h3><p>The presence of motion artifacts (MA) in cardiac signals negatively impacts the reliability of higher-level information such as the Heart Rate (HR), and therefore the correct diagnosis of pathologies. This paper proposes an MA detection method, based on One-Dimensional Convolutional Neural Networks (1D CNN), to label noisy zones of signals as unreliable, and subsequently avoid them for metric calculations.</p><h3 data-test=\\\"abstract-sub-heading\\\">Methods</h3><p>To validate the concept, we first design a CNN to detect MAs in electrocardiogram (ECG) recordings from MIT–BIH Arrhythmia and Noise Stress Test Databases. This network extracts features from 1 s data segments, and then classifies them as clean or noisy. Also, we then train a tuned version of the model with semi-synthetic ballistocardiogram (BCG) signals.</p><h3 data-test=\\\"abstract-sub-heading\\\">Results</h3><p>The classification in ECG achieves an accuracy of 95.9% and the BCG classification obtains an accuracy of 91.1%. Both classifiers are incorporated into beat detection systems, which produce an increase in the sensitivity of the detection algorithms from 75 to 98.5% in the ECG case, and from 72.1 to 94.5% in the case of BCG, for signals contaminated at 0 dB of SNR.</p><h3 data-test=\\\"abstract-sub-heading\\\">Conclusion</h3><p>We propose that this method will improve accuracy of any processing algorithm on BCG signals by identifying useful segments where a high accuracy can be achieved.</p>\",\"PeriodicalId\":50133,\"journal\":{\"name\":\"Journal of Medical and Biological Engineering\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.6000,\"publicationDate\":\"2024-06-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Medical and Biological Engineering\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1007/s40846-024-00872-w\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"ENGINEERING, BIOMEDICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Medical and Biological Engineering","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1007/s40846-024-00872-w","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENGINEERING, BIOMEDICAL","Score":null,"Total":0}
1D Convolutional Neural Network Impact on Heart Rate Metrics for ECG and BCG Signals
Purpose
The presence of motion artifacts (MA) in cardiac signals negatively impacts the reliability of higher-level information such as the Heart Rate (HR), and therefore the correct diagnosis of pathologies. This paper proposes an MA detection method, based on One-Dimensional Convolutional Neural Networks (1D CNN), to label noisy zones of signals as unreliable, and subsequently avoid them for metric calculations.
Methods
To validate the concept, we first design a CNN to detect MAs in electrocardiogram (ECG) recordings from MIT–BIH Arrhythmia and Noise Stress Test Databases. This network extracts features from 1 s data segments, and then classifies them as clean or noisy. Also, we then train a tuned version of the model with semi-synthetic ballistocardiogram (BCG) signals.
Results
The classification in ECG achieves an accuracy of 95.9% and the BCG classification obtains an accuracy of 91.1%. Both classifiers are incorporated into beat detection systems, which produce an increase in the sensitivity of the detection algorithms from 75 to 98.5% in the ECG case, and from 72.1 to 94.5% in the case of BCG, for signals contaminated at 0 dB of SNR.
Conclusion
We propose that this method will improve accuracy of any processing algorithm on BCG signals by identifying useful segments where a high accuracy can be achieved.
期刊介绍:
The purpose of Journal of Medical and Biological Engineering, JMBE, is committed to encouraging and providing the standard of biomedical engineering. The journal is devoted to publishing papers related to clinical engineering, biomedical signals, medical imaging, bio-informatics, tissue engineering, and so on. Other than the above articles, any contributions regarding hot issues and technological developments that help reach the purpose are also included.