Neonatal Heart and Lung Sound Quality Assessment for Robust Heart and Breathing Rate Estimation for Telehealth Applications

Grooby, Ethan; He, Jing; Kiewsky, Julie; Fattahi, Davood; Zhou, Lindsay; King, Arrabella; Ramanathan, Ashwin; Malhotra, Atul; Dumont, Guy A.; Marzbanrad, Faezeh

doi:10.1109/jbhi.2020.3047602

Cited by 37 publications

(69 citation statements)

References 43 publications

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“…Also in 2020, Chakraborty et al [37] published a work in which they used a convolutional neural network fed with the spectrogram of the PCG signal to evaluate the signal quality. In the same year, a paper by Grooby et al [38] proposed the extraction of as many as 187 features from neonatal PCG signals and classified their quality using an ensemble classifier combining a support vector machine, a decision tree, K-nearest neighbors, and a Gaussian Naïve Bayes classifier. A major limitation of this approach is the difficulty in relating most of the 187 features to cardiac physiology or to visually inspect signal features easily, thus binding the user to completely trust the methodology.…”

Section: Discussionmentioning

confidence: 99%

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

Giordano

Rosati

Knaflitz

2021

Sensors

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The signal quality limits the applicability of phonocardiography at the patients’ domicile. This work proposes the signal-to-noise ratio of the recorded signal as its main quality metrics. Moreover, we define the minimum acceptable values of the signal-to-noise ratio that warrantee an accuracy of the derived parameters acceptable in clinics. We considered 25 original heart sounds recordings, which we corrupted by adding noise to decrease their signal-to-noise ratio. We found that a signal-to-noise ratio equal to or higher than 14 dB warrants an uncertainty of the estimate of the valve closure latencies below 1 ms. This accuracy is higher than that required by most clinical applications. We validated the proposed method against a public database, obtaining results comparable to those obtained on our sample population. In conclusion, we defined (a) the signal-to-noise ratio of the phonocardiographic signal as the preferred metric to evaluate its quality and (b) the minimum values of the signal-to-noise ratio required to obtain an uncertainty of the latency of heart sound components compatible with clinical applications. We believe these results are crucial for the development of home monitoring systems aimed at preventing acute episodes of heart failure and that can be safely operated by naïve users.

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Section: Discussionmentioning

confidence: 99%

“…Finally, it should be highlighted that more than half of the cited works related to PCG quality assessment [11,12,14,19,[31][32][33][34][35][36][37][38]44] were published in the last 3 years, implying that this topic is gaining importance within the scientific community.…”

Section: Discussionmentioning

confidence: 99%

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

Giordano

Rosati

Knaflitz

2021

Sensors

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“…These features were then used to develop a dynamic classifier with 96% specificity, 81% sensitivity and 93% accuracy. These results were shown to be superior to the individual implementation of past heart signal quality estimation techniques [6].…”

Section: Introductionmentioning

confidence: 97%

“…However, low-quality chest sounds, due to the noise from either external environment, other internal body sounds, or the device itself, hinder the usage of stethoscopes. Lowquality recordings complicate monitoring and diagnosis, or at worse lead to misdiagnosis [6], [7]. Whilst having low-quality chest sounds is unavoidable, identification and exclusion of low-quality recordings help to improve remote monitoring.…”

Section: Introductionmentioning

confidence: 99%

“…Previous research on heart signal quality analysis has mainly focused on the binary classification of heart sounds into high and low-quality on adult populations. In our past work, these methods were reviewed in detail, adapted and expanded for the neonatal population [6]. To summarise, heart sound recordings were represented in several ways: time and frequency domain, autocorrelation signal, wavelet decomposed signal and segmented heart signal into S1 and S2 sounds [8]- [12].…”

Section: Introductionmentioning

confidence: 99%

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Real-Time Multi-Level Neonatal Heart and Lung Sound Quality Assessment for Telehealth Applications

et al. 2022

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In this study, a new method is proposed to assess heart and lung signal quality objectively and automatically on a 5-level scale in real-time, and to assess the effect of signal quality on vital sign estimation. A total of 207 10 s long chest sounds were taken from 119 preterm and full-term babies. Thirty of the recordings from ten subjects were obtained with synchronous vital signs from the Neonatal Intensive Care Unit (NICU). As a reference, seven annotators independently assessed the signal quality. For automatic quality classification, 400 features were extracted from the chest sounds. After feature ranking and selection, class balancing, and hyperparameter optimization, a variety of multi-class and ordinal classification and regression algorithms were trained. Then, heart rate and breathing rate were automatically estimated from the chest sounds. For the deep learning model, YAMNet, a deep convolutional neural network pre-trained on the AudioSet-Youtube corpus for sound classification was used. After modification of the final output layers of the neural network and class balancing, transfer learning was applied to YAMNet for heart and lung signal quality classification. The results of subject-wise leave-one-out cross-validation show that the best-performing models had a balanced accuracy of 56.8% and 51.2% for heart and lung qualities, respectively. The best-performing models for real-time analysis (<200 ms) had a balanced accuracy of 56.7% and 46.3%, respectively. Our experimental results underscore that increasing the signal quality leads to a reduction in vital sign error.

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A Two-Level Classifier for Prediction of Healthy and Unhealthy Lung Sounds Using Machine Learning and Convolutional Neural Network

Koshta,

Singh

2024

Communications in Computer and Information Science

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Neonatal Heart and Lung Sound Quality Assessment for Robust Heart and Breathing Rate Estimation for Telehealth Applications

Cited by 37 publications

References 43 publications

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

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

Real-Time Multi-Level Neonatal Heart and Lung Sound Quality Assessment for Telehealth Applications

A Two-Level Classifier for Prediction of Healthy and Unhealthy Lung Sounds Using Machine Learning and Convolutional Neural Network

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