1
|
Diab MS, Rodriguez-Villegas E. Feature evaluation of accelerometry signals for cough detection. Front Digit Health 2024; 6:1368574. [PMID: 38585283 PMCID: PMC10995234 DOI: 10.3389/fdgth.2024.1368574] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Accepted: 03/06/2024] [Indexed: 04/09/2024] Open
Abstract
Cough is a common symptom of multiple respiratory diseases, such as asthma and chronic obstructive pulmonary disorder. Various research works targeted cough detection as a means for continuous monitoring of these respiratory health conditions. This has been mainly achieved using sophisticated machine learning or deep learning algorithms fed with audio recordings. In this work, we explore the use of an alternative detection method, since audio can generate privacy and security concerns related to the use of always-on microphones. This study proposes the use of a non-contact tri-axial accelerometer for motion detection to differentiate between cough and non-cough events/movements. A total of 43 time-domain features were extracted from the acquired tri-axial accelerometry signals. These features were evaluated and ranked for their importance using six methods with adjustable conditions, resulting in a total of 11 feature rankings. The ranking methods included model-based feature importance algorithms, first principal component, leave-one-out, permutation, and recursive features elimination (RFE). The ranking results were further used in the feature selection of the top 10, 20, and 30 for use in cough detection. A total of 68 classification models using a simple logistic regression classifier are reported, using two approaches for data splitting: subject-record-split and leave-one-subject-out (LOSO). The best-performing model out of the 34 using subject-record-split obtained an accuracy of 92.20%, sensitivity of 90.87%, specificity of 93.52%, and F1 score of 92.09% using only 20 features selected by the RFE method. The best-performing model out of the 34 using LOSO obtained an accuracy of 89.57%, sensitivity of 85.71%, specificity of 93.43%, and F1 score of 88.72% using only 10 features selected by the RFE method. These results demonstrate the ability for future implementation of a motion-based wearable cough detector.
Collapse
Affiliation(s)
- Maha S. Diab
- Wearable Technologies Lab, Department of Electrical and Electronic Engineering, Imperial College London, London, United Kingdom
| | | |
Collapse
|
2
|
Sang B, Wen H, Junek G, Neveu W, Di Francesco L, Ayazi F. An Accelerometer-Based Wearable Patch for Robust Respiratory Rate and Wheeze Detection Using Deep Learning. BIOSENSORS 2024; 14:118. [PMID: 38534225 DOI: 10.3390/bios14030118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Revised: 02/17/2024] [Accepted: 02/20/2024] [Indexed: 03/28/2024]
Abstract
Wheezing is a critical indicator of various respiratory conditions, including asthma and chronic obstructive pulmonary disease (COPD). Current diagnosis relies on subjective lung auscultation by physicians. Enabling this capability via a low-profile, objective wearable device for remote patient monitoring (RPM) could offer pre-emptive, accurate respiratory data to patients. With this goal as our aim, we used a low-profile accelerometer-based wearable system that utilizes deep learning to objectively detect wheezing along with respiration rate using a single sensor. The miniature patch consists of a sensitive wideband MEMS accelerometer and low-noise CMOS interface electronics on a small board, which was then placed on nine conventional lung auscultation sites on the patient's chest walls to capture the pulmonary-induced vibrations (PIVs). A deep learning model was developed and compared with a deterministic time-frequency method to objectively detect wheezing in the PIV signals using data captured from 52 diverse patients with respiratory diseases. The wearable accelerometer patch, paired with the deep learning model, demonstrated high fidelity in capturing and detecting respiratory wheezes and patterns across diverse and pertinent settings. It achieved accuracy, sensitivity, and specificity of 95%, 96%, and 93%, respectively, with an AUC of 0.99 on the test set-outperforming the deterministic time-frequency approach. Furthermore, the accelerometer patch outperforms the digital stethoscopes in sound analysis while offering immunity to ambient sounds, which not only enhances data quality and performance for computational wheeze detection by a significant margin but also provides a robust sensor solution that can quantify respiration patterns simultaneously.
Collapse
Affiliation(s)
- Brian Sang
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - Haoran Wen
- StethX Microsystems Inc., Atlanta, GA 30308, USA
| | | | - Wendy Neveu
- Department of Medicine, Emory University School of Medicine, Atlanta, GA 30322, USA
| | - Lorenzo Di Francesco
- Department of Medicine, Emory University School of Medicine, Atlanta, GA 30322, USA
| | - Farrokh Ayazi
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
- StethX Microsystems Inc., Atlanta, GA 30308, USA
| |
Collapse
|
3
|
Pahar M, Miranda I, Diacon A, Niesler T. Automatic Non-Invasive Cough Detection based on Accelerometer and Audio Signals. JOURNAL OF SIGNAL PROCESSING SYSTEMS 2022; 94:821-835. [PMID: 35341095 PMCID: PMC8934184 DOI: 10.1007/s11265-022-01748-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 01/09/2022] [Accepted: 02/23/2022] [Indexed: 12/01/2022]
Abstract
We present an automatic non-invasive way of detecting cough events based on both accelerometer and audio signals. The acceleration signals are captured by a smartphone firmly attached to the patient’s bed, using its integrated accelerometer. The audio signals are captured simultaneously by the same smartphone using an external microphone. We have compiled a manually-annotated dataset containing such simultaneously-captured acceleration and audio signals for approximately 6000 cough and 68000 non-cough events from 14 adult male patients. Logistic regression (LR), support vector machine (SVM) and multilayer perceptron (MLP) classifiers provide a baseline and are compared with three deep architectures, convolutional neural network (CNN), long short-term memory (LSTM) network, and residual-based architecture (Resnet50) using a leave-one-out cross-validation scheme. We find that it is possible to use either acceleration or audio signals to distinguish between coughing and other activities including sneezing, throat-clearing, and movement on the bed with high accuracy. However, in all cases, the deep neural networks outperform the shallow classifiers by a clear margin and the Resnet50 offers the best performance, achieving an area under the ROC curve (AUC) exceeding 0.98 and 0.99 for acceleration and audio signals respectively. While audio-based classification consistently offers better performance than acceleration-based classification, we observe that the difference is very small for the best systems. Since the acceleration signal requires less processing power, and since the need to record audio is sidestepped and thus privacy is inherently secured, and since the recording device is attached to the bed and not worn, an accelerometer-based highly accurate non-invasive cough detector may represent a more convenient and readily accepted method in long-term cough monitoring.
Collapse
Affiliation(s)
- Madhurananda Pahar
- Department of Electrical and Electronic Engineering, Stellenbosch University, Stellenbosch, 7600 Western Cape South Africa
| | - Igor Miranda
- Federal University of Recôncavo da Bahia, Cruz das Almas, 44.380-000 Bahia Brazil
| | - Andreas Diacon
- TASK Applied Science, Cape Town, Western Cape South Africa
| | - Thomas Niesler
- Department of Electrical and Electronic Engineering, Stellenbosch University, Stellenbosch, 7600 Western Cape South Africa
| |
Collapse
|
4
|
da Costa BOI, Dantas AMX, Machado LDS, da Silva HJ, Pernambuco L, Lopes LW. Wearable technology use for the analysis and monitoring of functions related to feeding and communication. Codas 2022; 34:e20210278. [PMID: 35894374 PMCID: PMC9886183 DOI: 10.1590/2317-1782/20212021278pt] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2021] [Accepted: 11/18/2021] [Indexed: 02/03/2023] Open
Affiliation(s)
| | - Alana Moura Xavier Dantas
- Programa de Pós-graduação em Odontologia, Cidade Universitária, Universidade Federal de Pernambuco – UFPE - Recife (PE), Brasil.
| | - Liliane dos Santos Machado
- Programa de Pós-graduação em Modelos de Decisão e Saúde, Universidade Federal da Paraíba – UFPB - João Pessoa (PB), Brasil.
| | - Hilton Justino da Silva
- Programa de Pós-graduação em Odontologia, Cidade Universitária, Universidade Federal de Pernambuco – UFPE - Recife (PE), Brasil.
| | - Leandro Pernambuco
- Programa de Pós-graduação em Modelos de Decisão e Saúde, Universidade Federal da Paraíba – UFPB - João Pessoa (PB), Brasil.
| | - Leonardo Wanderley Lopes
- Programa de Pós-graduação em Modelos de Decisão e Saúde, Universidade Federal da Paraíba – UFPB - João Pessoa (PB), Brasil.
| |
Collapse
|
5
|
Costa BOID, Dantas AMX, Machado LDS, Silva HJD, Pernambuco L, Lopes LW. Wearable technology use for the analysis and monitoring of functions related to feeding and communication. Codas 2022. [DOI: 10.1590/2317-1782/20212021278en] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
|
6
|
O'Brien MK, Botonis OK, Larkin E, Carpenter J, Martin-Harris B, Maronati R, Lee K, Cherney LR, Hutchison B, Xu S, Rogers JA, Jayaraman A. Advanced Machine Learning Tools to Monitor Biomarkers of Dysphagia: A Wearable Sensor Proof-of-Concept Study. Digit Biomark 2021; 5:167-175. [PMID: 34723069 DOI: 10.1159/000517144] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2021] [Accepted: 05/10/2021] [Indexed: 11/19/2022] Open
Abstract
Introduction Difficulty swallowing (dysphagia) occurs frequently in patients with neurological disorders and can lead to aspiration, choking, and malnutrition. Dysphagia is typically diagnosed using costly, invasive imaging procedures or subjective, qualitative bedside examinations. Wearable sensors are a promising alternative to noninvasively and objectively measure physiological signals relevant to swallowing. An ongoing challenge with this approach is consolidating these complex signals into sensitive, clinically meaningful metrics of swallowing performance. To address this gap, we propose 2 novel, digital monitoring tools to evaluate swallows using wearable sensor data and machine learning. Methods Biometric swallowing and respiration signals from wearable, mechano-acoustic sensors were compared between patients with poststroke dysphagia and nondysphagic controls while swallowing foods and liquids of different consistencies, in accordance with the Mann Assessment of Swallowing Ability (MASA). Two machine learning approaches were developed to (1) classify the severity of impairment for each swallow, with model confidence ratings for transparent clinical decision support, and (2) compute a similarity measure of each swallow to nondysphagic performance. Task-specific models were trained using swallow kinematics and respiratory features from 505 swallows (321 from patients and 184 from controls). Results These models provide sensitive metrics to gauge impairment on a per-swallow basis. Both approaches demonstrate intrasubject swallow variability and patient-specific changes which were not captured by the MASA alone. Sensor measures encoding respiratory-swallow coordination were important features relating to dysphagia presence and severity. Puree swallows exhibited greater differences from controls than saliva swallows or liquid sips (p < 0.037). Discussion Developing interpretable tools is critical to optimize the clinical utility of novel, sensor-based measurement techniques. The proof-of-concept models proposed here provide concrete, communicable evidence to track dysphagia recovery over time. With refined training schemes and real-world validation, these tools can be deployed to automatically measure and monitor swallowing in the clinic and community for patients across the impairment spectrum.
Collapse
Affiliation(s)
- Megan K O'Brien
- Max Nader Lab for Rehabilitation Technologies and Outcomes Research, Shirley Ryan AbilityLab, Chicago, Illinois, USA.,Department of Physical Medicine and Rehabilitation, Northwestern University, Chicago, Illinois, USA
| | - Olivia K Botonis
- Max Nader Lab for Rehabilitation Technologies and Outcomes Research, Shirley Ryan AbilityLab, Chicago, Illinois, USA
| | - Elissa Larkin
- Think and Speak Lab, Shirley Ryan AbilityLab, Chicago, Illinois, USA
| | - Julia Carpenter
- Think and Speak Lab, Shirley Ryan AbilityLab, Chicago, Illinois, USA
| | - Bonnie Martin-Harris
- Department of Communication Sciences and Disorders, Northwestern University, Evanston, Illinois, USA
| | - Rachel Maronati
- Max Nader Lab for Rehabilitation Technologies and Outcomes Research, Shirley Ryan AbilityLab, Chicago, Illinois, USA
| | | | - Leora R Cherney
- Department of Physical Medicine and Rehabilitation, Northwestern University, Chicago, Illinois, USA.,Think and Speak Lab, Shirley Ryan AbilityLab, Chicago, Illinois, USA.,Department of Communication Sciences and Disorders, Northwestern University, Evanston, Illinois, USA
| | - Brianna Hutchison
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA
| | - Shuai Xu
- Departments of Materials Science and Engineering, Center for Bio-Integrated Electronics, Biomedical Engineering, Electrical Engineering and Computer Science, Northwestern University, Evanston, Illinois, USA
| | - John A Rogers
- Departments of Materials Science and Engineering, Center for Bio-Integrated Electronics, Biomedical Engineering, Electrical Engineering and Computer Science, Northwestern University, Evanston, Illinois, USA
| | - Arun Jayaraman
- Max Nader Lab for Rehabilitation Technologies and Outcomes Research, Shirley Ryan AbilityLab, Chicago, Illinois, USA.,Department of Physical Medicine and Rehabilitation, Northwestern University, Chicago, Illinois, USA
| |
Collapse
|