Enabling effective breathing sound analysis for automated diagnosis of lung diseases.

With the emergence of the COVID-19 pandemic, early diagnosis of lung diseases has attracted growing attention. Generally, monitoring the breathing sound is the traditional means for assessing the status of a patient's respiratory health through auscultation; for that a stethoscope is one of the clinical tools used by physicians for diagnosis of lung disease and anomalies. On the other hand, recent technological advances have made telehealth systems a practical and effective option for health status assessment and remote patient monitoring. The interest in telehealth solutions have further grown with the COVID-19 pandemic. These telehealth systems aim to provide increased safety and help to cope with the massive growth in healthcare demand. Particularly, employing acoustic sensors to collect breathing sound would enable real-time assessment and instantaneous detection of anomalies. However, existing work focuses on autonomous determination of respiratory rate which is not suitable for anomaly detection due to inability to deal with noisy data recording. This paper presents a novel approach for effective breathing sound analysis. We promote a new segmentation mechanism of the captured acoustic signals to identify breathing cycles in recorded sound signals. A scoring scheme is applied to qualify the segment based on the targeted respiratory illness by the overall breathing sound analysis. We demonstrate the effectiveness of our approach via experiments using published COPD datasets.

A wearable multi-modal acoustic system for breathing analysis.

Chronic obstructive pulmonary disease (COPD) is the third leading cause of death worldwide with over 3 × 106 deaths in 2019. Such an alarming figure becomes frightening when combined with the number of lost lives resulting from COVID-caused respiratory failure. Because COPD exacerbations identified early can commonly be treated at home, early symptom detections may enable a major reduction of COPD patient readmission and associated healthcare costs; this is particularly important during pandemics such as COVID-19 in which healthcare facilities are overwhelmed. The standard adjuncts used to assess lung function (e.g., spirometry, plethysmography, and CT scan) are expensive, time consuming, and cannot be used in remote patient monitoring of an acute exacerbation. In this paper, a wearable multi-modal system for breathing analysis is presented, which can be used in quantifying various airflow obstructions. The wearable multi-modal electroacoustic system employs a body area sensor network with each sensor-node having a multi-modal sensing capability, such as a digital stethoscope, electrocardiogram monitor, thermometer, and goniometer. The signal-to-noise ratio (SNR) of the resulting acoustic spectrum is used as a measure of breathing intensity. The results are shown from data collected from over 35 healthy subjects and 3 COPD subjects, demonstrating a positive correlation of SNR values to the health-scale score.

Smart Multimodal Telehealth-IoT System for COVID-19 Patients.

The COVID-19 pandemic has highlighted how the healthcare system could be overwhelmed. Telehealth stands out to be an effective solution, where patients can be monitored remotely without packing hospitals and exposing healthcare givers to the deadly virus. This article presents our Intel award winning solution for diagnosing COVID-19 related symptoms and similar contagious diseases. Our solution realizes an Internet of Things system with multimodal physiological sensing capabilities. The sensor nodes are integrated in a wearable shirt (vest) to enable continuous monitoring in a noninvasive manner; the data are collected and analyzed using advanced machine learning techniques at a gateway for remote access by a healthcare provider. Our system can be used by both patients and quarantined individuals. The article presents an overview of the system and briefly describes some novel techniques for increased resource efficiency and assessment fidelity. Preliminary results are provided and the roadmap for full clinical trials is discussed.