{"title":"Estimating Heart Rate Variability in Challenging Low SNR Regimes Using Wearable Magnetocardiography Sensors","authors":"Ali Kaiss;Md. Asiful Islam;Asimina Kiourti","doi":"10.1109/JERM.2024.3426270","DOIUrl":null,"url":null,"abstract":"We report <sc>Beat Estimation</small>, a novel method used to calculate Heart Rate Variability (HRV) from low Signal to Noise Ratio (SNR) data (−7 dB to −4 dB in this work) acquired via wearable magnetocardiography (MCG). MCG activity is first collected using an in-house wearable sensor and filtered to remove noise outside the band of interest. <sc>Beat Estimation</small> extracts a single heart beat from the filtered recording and correlates it with a small number of beats individually to average out the remaining noise. The de-noised beat is then correlated with the full recording to identify the location of each of the heart beats. Using these locations, HRV parameters are, finally, calculated. Results show <inline-formula><tex-math>$\\sim$</tex-math></inline-formula>99.9% accuracy in estimating HRV metrics using beat-to-beat intervals as opposed to traditional R-to-R-peak intervals. The average accuracy of detecting the true location of beats is shown to increase to 96.43% using <sc>Beat Estimation</small> as opposed to 59.98% using our previous method that relied on R-peak detection. In summary, <sc>Beat Estimation</small> renders wearable MCG sensors capable of accurately estimating HRV, despite the low SNR levels associated with sensor operation. The approach can be game-changing in assessing heart health, cardiovascular fitness, stress levels, cognitive workload, and more.","PeriodicalId":29955,"journal":{"name":"IEEE Journal of Electromagnetics RF and Microwaves in Medicine and Biology","volume":"9 1","pages":"27-35"},"PeriodicalIF":3.0000,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Journal of Electromagnetics RF and Microwaves in Medicine and Biology","FirstCategoryId":"1085","ListUrlMain":"https://ieeexplore.ieee.org/document/10601250/","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
Abstract
We report Beat Estimation, a novel method used to calculate Heart Rate Variability (HRV) from low Signal to Noise Ratio (SNR) data (−7 dB to −4 dB in this work) acquired via wearable magnetocardiography (MCG). MCG activity is first collected using an in-house wearable sensor and filtered to remove noise outside the band of interest. Beat Estimation extracts a single heart beat from the filtered recording and correlates it with a small number of beats individually to average out the remaining noise. The de-noised beat is then correlated with the full recording to identify the location of each of the heart beats. Using these locations, HRV parameters are, finally, calculated. Results show $\sim$99.9% accuracy in estimating HRV metrics using beat-to-beat intervals as opposed to traditional R-to-R-peak intervals. The average accuracy of detecting the true location of beats is shown to increase to 96.43% using Beat Estimation as opposed to 59.98% using our previous method that relied on R-peak detection. In summary, Beat Estimation renders wearable MCG sensors capable of accurately estimating HRV, despite the low SNR levels associated with sensor operation. The approach can be game-changing in assessing heart health, cardiovascular fitness, stress levels, cognitive workload, and more.