Automatic Lung Health Screening Using Respiratory Sounds

Mukherjee, Himadri; Sreerama, Priyanka; Dhar, Ankita; Obaidullah, Sk Md; Mahmud, Mufti; Santosh, K. C.

doi:10.1007/s10916-020-01681-9

Cited by 42 publications

(12 citation statements)

References 23 publications

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“…On the 2-class RDC task, our systems using stochastic normalization achieves the average scores of 93.77% and 98.20% for the official splitting and 5-fold cross-validation, respectively. Our best 2-class RDC system outperforms all compared systems on the former evaluation, but it scores 1.02% lower compared to the system in [39].…”

Section: Performance Comparison 1) Comparison To State-of-the-art Sys...mentioning

confidence: 78%

Lung Sound Classification Using Co-tuning and Stochastic Normalization

Nguyen¹,

Pernkopf²

2021

Preprint

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In this paper, we use pre-trained ResNet models as backbone architectures for classification of adventitious lung sounds and respiratory diseases. The knowledge of the pretrained model is transferred by using vanilla fine-tuning, cotuning, stochastic normalization and the combination of the cotuning and stochastic normalization techniques. Furthermore, data augmentation in both time domain and time-frequency domain is used to account for the class imbalance of the ICBHI and our multi-channel lung sound dataset. Additionally, we apply spectrum correction to consider the variations of the recording device properties on the ICBHI dataset. Empirically, our proposed systems mostly outperform all state-of-the-art lung sound classification systems for the adventitious lung sounds and respiratory diseases of both datasets.

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Section: Performance Comparison 1) Comparison To State-of-the-art Sys...mentioning

confidence: 78%

Lung Sound Classification Using Co-tuning and Stochastic Normalization

Nguyen¹,

Pernkopf²

2021

Preprint

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“…While some of those descriptions can easily be explained with singular features, e.g., lower speech fundamental frequency (sometimes called pitch) in the case of low voice, or lower number of voiced frames in an utterance (speech rate) and pauses between words in the case of slow voice, we often find more complex groups of features describing such phenomena as they can produce more information for such a varied process like speech. Such features can also help to identify other breathing abnormalities (e.g., Mukherjee et al, 2021a , b ).…”

Section: Predictionmentioning

confidence: 99%

AI-Based Prediction and Prevention of Psychological and Behavioral Changes in Ex-COVID-19 Patients

et al. 2021

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The COVID-19 pandemic has adverse consequences on human psychology and behavior long after initial recovery from the virus. These COVID-19 health sequelae, if undetected and left untreated, may lead to more enduring mental health problems, and put vulnerable individuals at risk of developing more serious psychopathologies. Therefore, an early distinction of such vulnerable individuals from those who are more resilient is important to undertake timely preventive interventions. The main aim of this article is to present a comprehensive multimodal conceptual approach for addressing these potential psychological and behavioral mental health changes using state-of-the-art tools and means of artificial intelligence (AI). Mental health COVID-19 recovery programs at post-COVID clinics based on AI prediction and prevention strategies may significantly improve the global mental health of ex-COVID-19 patients. Most COVID-19 recovery programs currently involve specialists such as pulmonologists, cardiologists, and neurologists, but there is a lack of psychiatrist care. The focus of this article is on new tools which can enhance the current limited psychiatrist resources and capabilities in coping with the upcoming challenges related to widespread mental health disorders. Patients affected by COVID-19 are more vulnerable to psychological and behavioral changes than non-COVID populations and therefore they deserve careful clinical psychological screening in post-COVID clinics. However, despite significant advances in research, the pace of progress in prevention of psychiatric disorders in these patients is still insufficient. Current approaches for the diagnosis of psychiatric disorders largely rely on clinical rating scales, as well as self-rating questionnaires that are inadequate for comprehensive assessment of ex-COVID-19 patients’ susceptibility to mental health deterioration. These limitations can presumably be overcome by applying state-of-the-art AI-based tools in diagnosis, prevention, and treatment of psychiatric disorders in acute phase of disease to prevent more chronic psychiatric consequences.

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“…BIS has previously been employed in the detection of pulmonary fluid accumulation in patients with HF [ 8 , 9 ]. Similarly, digital lung auscultation enables the quantification of acoustic properties that have long been used to distinguish cardiopulmonary disorders [ 10 , 11 ], and promising results have recently been reported from single- [ 12 ] and multi-channel lung sounds recordings [ 13 , 14 ]. Lastly, IP has been used to accurately estimate respiratory parameters which have shown relevance for assessing pulmonary function [ 15 , 16 ] and evaluating the risk of breathing disorders [ 17 ].…”

Section: Introductionmentioning

confidence: 99%

A Wearable Multimodal Sensing System for Tracking Changes in Pulmonary Fluid Status, Lung Sounds, and Respiratory Markers

Sanchez-Perez

Berkebile

Nevius

et al. 2022

Sensors

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Heart failure (HF) exacerbations, characterized by pulmonary congestion and breathlessness, require frequent hospitalizations, often resulting in poor outcomes. Current methods for tracking lung fluid and respiratory distress are unable to produce continuous, holistic measures of cardiopulmonary health. We present a multimodal sensing system that captures bioimpedance spectroscopy (BIS), multi-channel lung sounds from four contact microphones, multi-frequency impedance pneumography (IP), temperature, and kinematics to track changes in cardiopulmonary status. We first validated the system on healthy subjects (n = 10) and then conducted a feasibility study on patients (n = 14) with HF in clinical settings. Three measurements were taken throughout the course of hospitalization, and parameters relevant to lung fluid status—the ratio of the resistances at 5 kHz to those at 150 kHz (K)—and respiratory timings (e.g., respiratory rate) were extracted. We found a statistically significant increase in K (p < 0.05) from admission to discharge and observed respiratory timings in physiologically plausible ranges. The IP-derived respiratory signals and lung sounds were sensitive enough to detect abnormal respiratory patterns (Cheyne–Stokes) and inspiratory crackles from patient recordings, respectively. We demonstrated that the proposed system is suitable for detecting changes in pulmonary fluid status and capturing high-quality respiratory signals and lung sounds in a clinical setting.

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Automatic Lung Health Screening Using Respiratory Sounds

Cited by 42 publications

References 23 publications

Lung Sound Classification Using Co-tuning and Stochastic Normalization

Lung Sound Classification Using Co-tuning and Stochastic Normalization

AI-Based Prediction and Prevention of Psychological and Behavioral Changes in Ex-COVID-19 Patients

A Wearable Multimodal Sensing System for Tracking Changes in Pulmonary Fluid Status, Lung Sounds, and Respiratory Markers

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