Automated Assessment of the Quality of Phonocardographic Recordings through Signal-to-Noise Ratio for Home Monitoring Applications

Giordano, Noemi; Rosati, Samanta; Knaflitz, Marco

doi:10.3390/s21217246

Cited by 7 publications

(5 citation statements)

References 41 publications

(75 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The usability by inexperienced users was assessed by evaluating the percentage of recordings performed by the inexperienced enrolled volunteers showing a maximum SNR higher than 13 dB for S1 and higher than 14 dB for S2. In fact, we demonstrated in a previous study that an SNR higher than 13 dB for S1 and higher than 14 dB for S2 ensures an absolute error on the estimate lower than the temporal resolution of the recording system (1 ms) [33].…”

Section: Usability By Inexperienced Usersmentioning

confidence: 79%

See 1 more Smart Citation

A Wearable Multi-Sensor Array Enables the Recording of Heart Sounds in Homecare

Giordano

Rosati

Balestra

et al. 2023

Sensors

Self Cite

View full text Add to dashboard Cite

The home monitoring of patients affected by chronic heart failure (CHF) is of key importance in preventing acute episodes. Nevertheless, no wearable technological solution exists to date. A possibility could be offered by Cardiac Time Intervals extracted from simultaneous recordings of electrocardiographic (ECG) and phonocardiographic (PCG) signals. Nevertheless, the recording of a good-quality PCG signal requires accurate positioning of the stethoscope over the chest, which is unfeasible for a naïve user as the patient. In this work, we propose a solution based on multi-source PCG. We designed a flexible multi-sensor array to enable the recording of heart sounds by inexperienced users. The multi-sensor array is based on a flexible Printed Circuit Board mounting 48 microphones with a high spatial resolution, three electrodes to record an ECG and a Magneto-Inertial Measurement Unit. We validated the usability over a sample population of 42 inexperienced volunteers and found that all subjects could record signals of good to excellent quality. Moreover, we found that the multi-sensor array is suitable for use on a wide population of at-risk patients regardless of their body characteristics. Based on the promising findings of this study, we believe that the described device could enable the home monitoring of CHF patients soon.

show abstract

Section: Usability By Inexperienced Usersmentioning

confidence: 79%

“…The quality assessment was performed by means of the Signal-to-Noise Ratio (SNR) of respectively the first and second heart sound. For each channel and each heartbeat, the SNR was defined as in Equation ( 1) [33]:…”

Section: Signal Processingmentioning

confidence: 99%

A Wearable Multi-Sensor Array Enables the Recording of Heart Sounds in Homecare

Giordano

Rosati

Balestra

et al. 2023

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…Giordano et al [25] conducted an intelligent evaluation of PCG signals using SNR. This is well-suited for home monitoring systems.…”

Section: A Transform Based Methodsmentioning

confidence: 99%

Adopting Artificial Intelligence Algorithms for Remote Fetal Heart Rate Monitoring and Classification using Wearable Fetal Phonocardiography

ABBURI¹,

Hatai²,

Samanta³

2023

Preprint

View full text Add to dashboard Cite

<p>This work utilizes a wearable phonocardiogram belt for acquiring signals from remote areas. Four sequential methodologies are undergone in the proposed work for resilience, and effectiveness. Intelligent, and accurate FHR monitoring. At first, the noise and artifacts are removed by performing two levels pre-processing methodology method. The low frequency noises are removed by Chebyshev II High Pass Filter and high frequency noises are removed by a hybrid of EC2EMDAN- PS-MODWT filters respectively. In the next step, the denoised signals are segmented for reducing the complexity in which the segmentation is performed using Multi Agent Deep Q-Learning (MA-DQL) algorithm based on several constraints. Further, the segmented signal is provided for reducing the redundancies in cardiac cycles using Artificial Humming Bird Optimization (AHBO) algorithm. The segmented and non-redundant signals are converted into 3D spectrograms using a machine learning algorithm named Variational Auto Encoder-General Adversarial Networks (VAE-GAN) for analyzing the signals with better visual interpretation in 3D space. Finally, utilizing the 3D spectrogram analysis the feature extraction and classification is taken place by adopting a hybrid of Bidirectional Gated Recurrent Unit (BiGRU) and Multi Boosted Capsule Network (MBCapsNet) into three classes such as normal (110-160 BPM), abnormal (above 180 BPM), and Suspicious (fluctuates between normal and abnormal). The proposed work is implemented and simulated in the MATLAB R2020a tool and the performance is validated by adopting effective validation metrics. The outcomes demonstrate that the suggested work performs better than the current works. </p>

show abstract

“…For each heartbeat, the quality of the signal segments was assessed by evaluating their SNR. We discarded the signal segments presenting a SNR value lower than 10 dB because we previously demonstrated that a lower SNR is not sufficient for the application of interest [8].…”

Section: Estimation Of the Latenciesmentioning

confidence: 99%

Automatic Identification of the Best Auscultation Area for the Estimation of the Time of Closure of Heart Valves through Multi-Source Phonocardiography

Giordano¹,

Balestra²,

Ghislieri³

et al. 2022

Computing in Cardiology Conference (CinC)

View full text Add to dashboard Cite

In the latest years, multi-source phonocardiography (PCG) is gaining interest in relation to the home monitoring of cardiovascular diseases. An application of interest regards the monitoring of the time of closure of the four cardiac valves, which would enable the follow-up of at-risk patients for heart failure. In this work, we propose a hybrid system based on hierarchical clustering and Multi-Criteria Decision Analysis (MCDA) for automatically selecting the best auscultation area for the mentioned application through multi-source PCG. We simultaneously recorded 48 PCG signals from the subject's chest and divided them into morphologically homogenous groups using agglomerative hierarchical clustering, based on their correlation. Then, we explored three different approaches to select the best auscultation area, based respectively on the minimum latency, on the maximum signal-to-noise ratio, and on multiple criteria using ELECTRE III. The results obtained on the follow-up of a healthy subject over consecutive days show that a) the selection of the auscultation area using MCDA overcomes the limits of single-criteria approaches, b) the estimate of the time of closure of the heart valves using the proposed system is more robust than what obtained through the state-of-the-art single-source methodology.

show abstract

Automated Assessment of the Quality of Phonocardographic Recordings through Signal-to-Noise Ratio for Home Monitoring Applications

Cited by 7 publications

References 41 publications

A Wearable Multi-Sensor Array Enables the Recording of Heart Sounds in Homecare

A Wearable Multi-Sensor Array Enables the Recording of Heart Sounds in Homecare

Adopting Artificial Intelligence Algorithms for Remote Fetal Heart Rate Monitoring and Classification using Wearable Fetal Phonocardiography

Automatic Identification of the Best Auscultation Area for the Estimation of the Time of Closure of Heart Valves through Multi-Source Phonocardiography

Contact Info

Product

Resources

About