Automatic discrimination between cough and non-cough accelerometry signal artefacts

Mohammadi, Helia; Samadani, Ali-Akbar; Steele, Catriona M.; Chau, Tom

doi:10.1016/j.bspc.2018.10.013

Cited by 27 publications

(24 citation statements)

References 35 publications

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“…Noninvasive detection of swallowing impairment has garnered increasing interest in recent years, likely bolstered by advances in sensor technology and machine learning techniques. A primary focus has been accelerometry [15,17,28,29] or audio signals (i.e., microphone) to classify impairment via swallowing, coughing, and other behaviors [19,[30][31][32]. Results are promising; in a recent prospective study of 344 individuals at risk for oropharyn- geal dysphagia, Steele et al [17] trained a regularized linear discriminant analysis on dual-axis accelerometer signals and videofluoroscopy, achieving ∼90% sensitivity and ∼60% specificity for detecting impaired swallow safety (when material entered the airway; "penetration-aspiration") and ∼80% sensitivity and ∼60% specificity for detecting impaired swallow efficiency (when material remained in the pharynx).…”

Section: Discussionmentioning

confidence: 99%

Advanced Machine Learning Tools to Monitor Biomarkers of Dysphagia: A Wearable Sensor Proof-of-Concept Study

et al. 2021

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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.

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Section: Discussionmentioning

confidence: 99%

Advanced Machine Learning Tools to Monitor Biomarkers of Dysphagia: A Wearable Sensor Proof-of-Concept Study

et al. 2021

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“…Classification techniques aim to categorise sounds into cough and non-cough events. Artificial neural networks (ANN) are algorithms that attempt to simulate the behaviour of the human brain, and have been applied to attempt to differentiate between cough and non-cough events (56), cough segments and swallow signals, rest states and different non-cough artefacts (73), and to differentiate between cough, speech and noise (74). Recently, with advances in deep learning techniques, new approaches to cough detection have been proposed (55,62,75).…”

Section: Classificationmentioning

confidence: 99%

The present and future of cough counting tools

Hall¹,

Lozano²,

Estrada-Petrocelli³

et al. 2020

J Thorac Dis

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The widespread use of cough counting tools has, to date, been limited by a reliance on human input to determine cough frequency. However, over the last two decades advances in digital technology and audio capture have reduced this dependence. As a result, cough frequency is increasingly recognised as a measurable parameter of respiratory disease. Cough frequency is now the gold standard primary endpoint for trials of new treatments for chronic cough, has been investigated as a marker of infectiousness in tuberculosis (TB), and used to demonstrate recovery in exacerbations of chronic obstructive pulmonary disease (COPD).This review discusses the principles of automatic cough detection and summarises key currently and recently used cough counting technology in clinical research. It additionally makes some predictions on future directions in the field based on recent developments. It seems likely that newer approaches to signal processing, the adoption of techniques from automatic speech recognition, and the widespread ownership of mobile devices will help drive forward the development of real-time fully automated ambulatory cough frequency monitoring over the coming years. These changes should allow cough counting systems to transition from their current status as a niche research tool in chronic cough to a much more widely applicable method for assessing, investigating and understanding respiratory disease.

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“…Automatic cough detection and classification is possible by applying machine learning algorithms on extracted features from cough sounds [6]. It has also been shown to be possible when using the signals from an accelerometer placed on the patient's body [7]. Since the accelerometer is insensitive to environmental and background noise, it can be used in conjunction with other sensors such as microphones, ECG and thermistors [8].…”

Section: Introductionmentioning

confidence: 99%

“…Here the recorded signal is transmitted to a receiver carried in a pocket or attached to a belt. Throat-mounted accelerometers have been used successfully to detect coughing in [11] and in [12], and an accelerometer placed at the laryngeal prominence (Adam's apple) in [7]. Two accelerometers, one placed on the abdomen and the second on a belt wrapped at dorsal region, have been used to measure cough rate in the research carried out by [13].…”

Section: Introductionmentioning

confidence: 99%

Deep Neural Network Based Cough Detection Using Bed-Mounted Accelerometer Measurements

Pahar

Miranda

Diacon

et al. 2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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We have performed cough detection based on measurements from an accelerometer attached to the patient's bed. This form of monitoring is less intrusive than body-attached accelerometer sensors, and sidesteps privacy concerns encountered when using audio for cough detection. For our experiments, we have compiled a manually-annotated dataset containing the acceleration signals of approximately 6000 cough and 68000 non-cough events from 14 adult male patients in a tuberculosis clinic. As classifiers, we have considered convolutional neural networks (CNN), long-short-term-memory (LSTM) networks, and a residual neural network (Resnet50). We find that all classifiers are able to distinguish between the acceleration signals due to coughing and those due to other activities including sneezing, throat-clearing and movement in the bed with high accuracy. The Resnet50 performs the best, achieving an area under the ROC curve (AUC) exceeding 0.98 in cross-validation experiments. We conclude that high-accuracy cough monitoring based only on measurements from the accelerometer in a consumer smartphone is possible. Since the need to gather audio is avoided and therefore privacy is inherently protected, and since the accelerometer is attached to the bed and not worn, this form of monitoring may represent a more convenient and readily accepted method of long-term patient cough monitoring.

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Automatic discrimination between cough and non-cough accelerometry signal artefacts

Cited by 27 publications

References 35 publications

Advanced Machine Learning Tools to Monitor Biomarkers of Dysphagia: A Wearable Sensor Proof-of-Concept Study

Advanced Machine Learning Tools to Monitor Biomarkers of Dysphagia: A Wearable Sensor Proof-of-Concept Study

The present and future of cough counting tools

Deep Neural Network Based Cough Detection Using Bed-Mounted Accelerometer Measurements

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