Y. Rong, Matthew Fynn, S. Nordholm, Serena Siaw, G. Dwivedi
{"title":"用于心血管疾病监测的可穿戴式心电描记仪","authors":"Y. Rong, Matthew Fynn, S. Nordholm, Serena Siaw, G. Dwivedi","doi":"10.1109/SSP53291.2023.10208027","DOIUrl":null,"url":null,"abstract":"In this paper, we present a new wearable multichannel phonocardiography (PCG) and electrocardiography (ECG) device for cardiovascular disease (CVD) pre-screening and monitoring developed recently by researchers at Curtin University in collaboration with Ticking Heart, a health-tech start-up. An iterative Wiener filter based noise cancelation algorithm is proposed to improve the integrity of heart sound signals. We show that compared with an existing approach, the proposed algorithm has a better performance in suppressing the noise at 200-300 Hz. A convolutional neural network based classifier is implemented which exploits both the ECG and PCG signals to improve the pre-screening accuracy of CVD.","PeriodicalId":296346,"journal":{"name":"2023 IEEE Statistical Signal Processing Workshop (SSP)","volume":"77 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Wearable Electro-Phonocardiography Device for Cardiovascular Disease Monitoring\",\"authors\":\"Y. Rong, Matthew Fynn, S. Nordholm, Serena Siaw, G. Dwivedi\",\"doi\":\"10.1109/SSP53291.2023.10208027\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we present a new wearable multichannel phonocardiography (PCG) and electrocardiography (ECG) device for cardiovascular disease (CVD) pre-screening and monitoring developed recently by researchers at Curtin University in collaboration with Ticking Heart, a health-tech start-up. An iterative Wiener filter based noise cancelation algorithm is proposed to improve the integrity of heart sound signals. We show that compared with an existing approach, the proposed algorithm has a better performance in suppressing the noise at 200-300 Hz. A convolutional neural network based classifier is implemented which exploits both the ECG and PCG signals to improve the pre-screening accuracy of CVD.\",\"PeriodicalId\":296346,\"journal\":{\"name\":\"2023 IEEE Statistical Signal Processing Workshop (SSP)\",\"volume\":\"77 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-07-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2023 IEEE Statistical Signal Processing Workshop (SSP)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SSP53291.2023.10208027\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 IEEE Statistical Signal Processing Workshop (SSP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SSP53291.2023.10208027","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Wearable Electro-Phonocardiography Device for Cardiovascular Disease Monitoring
In this paper, we present a new wearable multichannel phonocardiography (PCG) and electrocardiography (ECG) device for cardiovascular disease (CVD) pre-screening and monitoring developed recently by researchers at Curtin University in collaboration with Ticking Heart, a health-tech start-up. An iterative Wiener filter based noise cancelation algorithm is proposed to improve the integrity of heart sound signals. We show that compared with an existing approach, the proposed algorithm has a better performance in suppressing the noise at 200-300 Hz. A convolutional neural network based classifier is implemented which exploits both the ECG and PCG signals to improve the pre-screening accuracy of CVD.