Wearable Cardiorespiratory Monitoring Employing a Multimodal Digital Patch Stethoscope: Estimation of ECG, PEP, LVETand Respiration Using a 55 mm Single-Lead ECG and Phonocardiogram.

Michael Klum,Mike Urban,Alexandru-Gabriel Pielmus,Theresa Schmitt,Reinhold Orglmeister,Aarne Feldheiser,Timo Tigges

doi:10.3390/s20072033

Michael Klum, Mike Urban + Show 5 more

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https://doi.org/10.3390/s20072033

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Abstract

Cardiovascular diseases are the main cause of death worldwide, with sleep disordered breathing being a further aggravating factor. Respiratory illnesses are the third leading cause of death amongst the noncommunicable diseases. The current COVID-19 pandemic, however, also highlights the impact of communicable respiratory syndromes. In the clinical routine, prolonged postanesthetic respiratory instability worsens the patient outcome. Even though early and continuous, long-term cardiorespiratory monitoring has been proposed or even proven to be beneficial in several situations, implementations thereof are sparse. We employed our recently presented, multimodal patch stethoscope to estimate Einthoven electrocardiogram (ECG) Lead I and II from a single 55 mm ECG lead. Using the stethoscope and ECG subsystems, the pre-ejection period (PEP) and left ventricular ejection time (LVET) were estimated. ECG-derived respiration techniques were used in conjunction with a novel, phonocardiogram-derived respiration approach to extract respiratory parameters. Medical-grade references were the SOMNOmedics SOMNO HDTM and Osypka ICON-CoreTM. In a study including 10 healthy subjects, we analyzed the performances in the supine, lateral, and prone position. Einthoven I and II estimations yielded correlations exceeding 0.97. LVET and PEP estimation errors were 10% and 21%, respectively. Respiratory rates were estimated with mean absolute errors below 1.2 bpm, and the respiratory signal yielded a correlation of 0.66. We conclude that the estimation of ECG, PEP, LVET, and respiratory parameters is feasible using a wearable, multimodal acquisition device and encourage further research in multimodal signal fusion for respiratory signal estimation.

Highlights

As well as multi-layer perceptrons (MLP) performed significantly worse than time-delay neural networks (TDNN), only a selection of the results of the TDNNs is shown
We found that the performance of the left ventricular ejection time (LVET) and pre-ejection period (PEP) estimations differed between different positions
It was shown that all three applications could be covered using a 55 mm single lead ECG integrated into a patch stethoscope system in the supine, lateral, and prone positions

Summary

Introduction

Clinical Background and State-of-the-Art. Of all worldwide deaths, 31.3% were caused by cardiovascular diseases (CVD) in 2016, making them the most common cause of death. Respiratory issues are the third leading cause of death worldwide with 6.7% [1]. It is a well-known fact, that respiration-related illnesses can significantly increase the risk for CVD and other diseases. In the case of obstructive sleep apnea (OSA), close relations to increased CVD such as hypertension [2]. It has been shown that respiratory complications are major causes of prolonged hospital stay, poor overall outcomes, and increased mortality [5,6,7]. It becomes evident that cardiorespiratory monitoring is one of the major fields to be addressed in the future

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