Acoustic cardiac signals analysis: a Kalman filter-based approach

Improved recovery of cardiac auscultation sounds using modified cosine transform and LSTM-based masking

Obtaining accurate cardiac auscultation signals, including basic heart sounds (S1 and S2) and subtle signs of disease, is crucial for improving cardiac diagnoses and making the most of telehealth. This research paper introduces an innovative approach that utilizes a modified cosine transform (MCT) and a masking strategy based on long short-term memory (LSTM) to effectively distinguish heart sounds and murmurs from background noise and interfering sounds. The MCT is used to capture the repeated pattern of the heart sounds, while the LSTMs are trained to construct masking based on the repeated MCT spectrum. The proposed strategy's performance in maintaining the clinical relevance of heart sounds continues to demonstrate effectiveness, even in environments marked by increased noise and complex disruptions. The present work highlights the clinical significance and reliability of the suggested methodology through in-depth signal visualization and rigorous statistical performance evaluations. In comparative assessments, the proposed approach has demonstrated superior performance compared to recent algorithms, such as LU-Net and PC-DAE. Furthermore, the system's adaptability to various datasets enhances its reliability and practicality. The suggested method is a potential way to improve the accuracy of cardiovascular diagnostics in an era of rapid advancement in medical signal processing. The proposed approach showed an enhancement in the average signal-to-noise ratio (SNR) by 9.6 dB at an input SNR of - 6 dB and by 3.3 dB at an input SNR of 10 dB. The average signal distortion ratio (SDR) achieved across a variety of input SNR values was 8.56 dB.

Accurate Modeling of Ejection Fraction and Stroke Volume With Mobile Phone Auscultation: Prospective Case-Control Study

BACKGROUND

Heart failure (HF) contributes greatly to morbidity, mortality, and health care costs worldwide. Hospital readmission rates are tracked closely and determine federal reimbursement dollars. No current modality or technology allows for accurate measurement of relevant HF parameters in ambulatory, rural, or underserved settings. This limits the use of telehealth to diagnose or monitor HF in ambulatory patients.

OBJECTIVE

This study describes a novel HF diagnostic technology using audio recordings from a standard mobile phone.

METHODS

This prospective study of acoustic microphone recordings enrolled convenience samples of patients from 2 different clinical sites in 2 separate areas of the United States. Recordings were obtained at the aortic (second intercostal) site with the patient sitting upright. The team used recordings to create predictive algorithms using physics-based (not neural networks) models. The analysis matched mobile phone acoustic data to ejection fraction (EF) and stroke volume (SV) as evaluated by echocardiograms. Using the physics-based approach to determine features eliminates the need for neural networks and overfitting strategies entirely, potentially offering advantages in data efficiency, model stability, regulatory visibility, and physical insightfulness.

RESULTS

Recordings were obtained from 113 participants. No recordings were excluded due to background noise or for any other reason. Participants had diverse racial backgrounds and body surface areas. Reliable echocardiogram data were available for EF from 113 patients and for SV from 65 patients. The mean age of the EF cohort was 66.3 (SD 13.3) years, with female patients comprising 38.3% (43/113) of the group. Using an EF cutoff of ≤40% versus >40%, the model (using 4 features) had an area under the receiver operating curve (AUROC) of 0.955, sensitivity of 0.952, specificity of 0.958, and accuracy of 0.956. The mean age of the SV cohort was 65.5 (SD 12.7) years, with female patients comprising 34% (38/65) of the group. Using a clinically relevant SV cutoff of <50 mL versus >50 mL, the model (using 3 features) had an AUROC of 0.922, sensitivity of 1.000, specificity of 0.844, and accuracy of 0.923. Acoustics frequencies associated with SV were observed to be higher than those associated with EF and, therefore, were less likely to pass through the tissue without distortion.

CONCLUSIONS

This work describes the use of mobile phone auscultation recordings obtained with unaltered cellular microphones. The analysis reproduced the estimates of EF and SV with impressive accuracy. This technology will be further developed into a mobile app that could bring screening and monitoring of HF to several clinical settings, such as home or telehealth, rural, remote, and underserved areas across the globe. This would bring high-quality diagnostic methods to patients with HF using equipment they already own and in situations where no other diagnostic and monitoring options exist.

2017 ISHNE-HRS expert consensus statement on ambulatory ECG and external cardiac monitoring/telemetry

Ambulatory ECG (AECG) is very commonly employed in a variety of clinical contexts to detect cardiac arrhythmias and/or arrhythmia patterns which are not readily obtained from the standard ECG. Accurate and timely characterization of arrhythmias is crucial to direct therapies that can have an important impact on diagnosis, prognosis or patient symptom status. The rhythm information derived from the large variety of AECG recording systems can often lead to appropriate and patient-specific medical and interventional management. The details in this document provide background and framework from which to apply AECG techniques in clinical practice, as well as clinical research.

Benefits of monitoring patients with mobile cardiac telemetry (MCT) compared with the Event or Holter monitors

INTRODUCTION

This research is meant to establish if a patient monitored with mobile cardiac telemetry (MCT) sees different outcomes regarding diagnostic yield of arrhythmia, therapeutic management through the use of antiarrhythmic drugs, and cardiovascular costs incurred in the hospital setting when compared with more traditional monitoring devices, such as the Holter or the Event monitor.

MATERIALS AND METHODS

We conducted a retrospective analysis spanning 57 months of claims data from January 2007 to September 2011 pertaining to 200,000+ patients, of whom 14,000 used MCT only, 54,000 an Event monitor only, and 163,000 a Holter monitor only. Those claims came from the Truven database, an employer database that counts 2.8 million cardiovascular patients from an insured population of about 10 million members. We employed a pair-wise pre/post test-control methodology, and ensured that control patients were similar to test patients along the following dimensions: age, geographic location, type of cardiovascular diagnosis both in the inpatient and outpatient settings, and the cardiovascular drug class the patient uses.

RESULTS

First, the diagnostic yield of patients monitored with MCT is 61%, that is significantly higher than that of patients that use the Event monitor (23%) or the Holter monitor (24%). Second, patients naive to antiarrhythmic drugs initiate drug therapy after monitoring at the following rates: 61% for patients that use MCT compared with 39% for patients that use the Event and 43% for patients that use the Holter. Third, there are very significant inpatient cardiovascular savings (in the tens of thousands of dollars) for patients that undergo ablation, coronary artery bypass graft (CABG) and valve septa. Savings are more modest but nonetheless significant when it comes to the heart/pericardium procedure.

CONCLUSION

Given the superior outcome of MCT regarding both patient care and hospital savings, hospitals only stand to gain by enforcing protocols that favor the MCT system over the Event or the Holter monitor.