A comparison of the power of breathing sounds signals acquired with a smart stethoscope from a cohort of COVID-19 patients at peak disease, and pre-discharge from the hospital

Hybrid method for noise rejection from breath sound using transient artifact reduction algorithm and spectral subtraction

OBJECTIVES

Computerized breath sound based diagnostic methods are one of the emerging technologies gaining popularity in terms of detecting respiratory disorders. However, the breath sound signal used in such automated systems used to be too noisy, which affects the quality of the diagnostic interpretations. To address this problem, the proposed work presents the new hybrid approach to reject the noises from breath sound.

METHODS

In this method, 80 chronic obstructive pulmonary disease (COPD), 75 asthmatics and 80 normal breath sounds were recorded from the participants of a hospital. Each of these breath sound data were decontaminated using hybrid method of Butterworth band-pass filter, transient artifact reduction algorithm and spectral subtraction algorithm. The study examined the algorithms noise rejection potential over each category of breath sound by estimating the noise rejection performance metrics, i.e., mean absolute error (MAE), mean square error (MSE), peak signal to noise ratio (PSNR), and signal to noise ratio (SNR).

RESULTS

Using this algorithm, the study obtained a high value of SNR of 70 dB and that of PSNR of 72 dB.

CONCLUSIONS

The study could definitely a suitable one to suppress noises and to produce noise free breath sound signal.

Feasibility of snapshot testing using wearable sensors to detect cardiorespiratory illness (COVID infection in India)

The COVID-19 pandemic has challenged the current paradigm of clinical and community-based disease detection. We present a multimodal wearable sensor system paired with a two-minute, movement-based activity sequence that successfully captures a snapshot of physiological data (including cardiac, respiratory, temperature, and percent oxygen saturation). We conducted a large, multi-site trial of this technology across India from June 2021 to April 2022 amidst the COVID-19 pandemic (Clinical trial registry name: International Validation of Wearable Sensor to Monitor COVID-19 Like Signs and Symptoms; NCT05334680; initial release: 04/15/2022). An Extreme Gradient Boosting algorithm was trained to discriminate between COVID-19 infected individuals (n = 295) and COVID-19 negative healthy controls (n = 172) and achieved an F1-Score of 0.80 (95% CI = [0.79, 0.81]). SHAP values were mapped to visualize feature importance and directionality, yielding engineered features from core temperature, cough, and lung sounds as highly important. The results demonstrated potential for data-driven wearable sensor technology for remote preliminary screening, highlighting a fundamental pivot from continuous to snapshot monitoring of cardiorespiratory illnesses.

A novel infrasound and audible machine-learning approach for the diagnosis of COVID-19

The COVID-19 outbreak has rapidly spread around the world, causing a global public health and economic crisis. A critical limitation in detecting COVID-19 related pneumonia is that it is often manifested as a “silent pneumonia”, i.e., pulmonary auscultation, using a standard stethoscope, sounds "normal". Chest CT is the gold standard for detecting COVID-19 pneumonia; however, radiation exposure, availability and cost preclude its utilization as a screening tool for COVID-19 pneumonia. In this study we hypothesized that COVID-19 pneumonia, “silent” to the human ear using a standard stethoscope, is detectable using a full spectrum auscultation device that contains a machine-learning analysis.

Lung sounds signals were acquired, using a novel full spectrum (3–2,000Hz) stethoscope, from 164 patients with COVID-19 pneumonia, 61 non-COVID-19 pneumonia and 141 healthy subjects. A machine-learning classifier was constructed, and the data was classified into 3 groups: 1. Normal lung sounds 2. COVID-19 pneumonia 3. Non-COVID-19 pneumonia.

Standard auscultation found that 72% of the non-COVID-19 pneumonia patients had abnormal lung sounds, compared to only 25% for the COVID-19 pneumonia patients. The classifier's sensitivity and specificity for the detection of COVID-19 pneumonia were 97% and 93%, respectively, when analyzing the sound and infrasound data, and they were reduced to 93% and 80% without the infrasound data (p<0.01 difference in ROC with and without infrasound).

This study reveals that useful clinical information exists in the infrasound spectrum of COVID-19 related pneumonia, and machine-learning analysis applied to the full spectrum of lung sounds is useful in its detection.