Preliminary results of a single, easy-to-use device for multimodal detection of intervals associated with heart failure decompensation
David Sánchez-Ortiz, Mercedes Rivas-Lasarte, Alejandro Nistal-Juarez, Luis Blanco-Tapia, Miguel Fribourg, Manuel Gómez-Bueno, Francisco José Hernández-Pérez, Cristina Mitroi, Alba Martin-Centellas, Ramón Garrido-González, Martín Santos-González, Javier Segovia-CuberoAbstract
Aims
The use of cardiac signal-recording tools to monitor prognostic and diagnostic parameters in heart failure is an emerging field, constrained by usability and a low signal-to-noise environment. We evaluated a compact, user-friendly prototype for simultaneous acquisition of PCG, PPG, ECG and subsequent processing, to determine this strategy’s usefulness in identifying parameters related to HF decompensation
Methods and results
We conducted a single-centre prospective study of 41 hospitalised patients; 36 included in the analysis. Autonomous recordings of phonocardiography, electrocardiography, and pulse-wave photoplethysmography were obtained. Signal processing employed a new supervised iterative filtering pipeline based on detection of coupled energy peaks, together with an independent analysis of the pulse-wave plethysmogram, enabling extraction of clinically relevant intervals even when one modality was of insufficient quality. In the cohort admitted with decompensated heart failure, measures at discharge showed shortening of electromechanical coupling time (0.148 ± 0.051 v. 0.108 ± 0.039 s; p < 0.001), isovolumetric contraction time (0.152 ± 0.098 v. 0.071 ± 0.023 s; p < 0.001) and ejection period (0.257 ± 0.078 v. 0.344 ± 0.082 s; p < 0.0001) compared with admission. These findings were corroborated against the cardiology inpatient cohort without clinical congestion and contextualised using a publicly available cohort of 338 healthy individuals.
Conclusions
This study presents initial evidence that a compact, easy-to-use device permits autonomous acquisition of diagnostic cardiac signals by non-expert users and that the proposed processing strategy reliably detects clinically relevant intervals in low-SNR recordings, supporting its potential utility for monitoring patients with heart failure.