Post-Stroke Respiratory Complications Using Machine Learning with Voice Capabilities from Mobile Devices

  • Warnecke, T. et al. Neurogenic dysphagia: systematic review and proposal of a classification system. Neurology 96e876–e889 (2021).

    CAS PubMed Google Scholar

  • Armstrong, JR & Mosher, BD Aspiration pneumonia after stroke: intervention and prevention. Neurohospitalist 185–93 (2011).

    Google Scholar article

  • Park, HY et al. Potential prognostic impact of dopamine D1 receptor (rs4532) polymorphism on post-stroke outcomes in the elderly. Front. Neurol. 12675060 (2021).

    Google Scholar article

  • Daniels, SK, Ballo, LA, Mahoney, MC & Foundas, AL Clinical predictors of dysphagia and aspiration risk: outcome measures in patients with acute stroke. Camber. Phys. Med. Rehabilit. 811030-1033 (2000).

    CAS Google Scholar Article

  • Groves-Wright, KJ, Boyce, S. & Kelchner, L. Perception of wet voice quality in identifying penetration/aspiration during swallowing. J. Speech. Lang. To listen. Res. 53620–632 (2010).

    Google Scholar article

  • Homer, J., Massey, EW, Riski, JE, Lathrop, DL & Chase, KN Aspiration after stroke: clinical correlates and outcome. Neurology 381359-1359 (1988).

    Google Scholar article

  • McCullough, GH, Wertz, RT & Rosenbek, JC Sensitivity and specificity of clinical/bedside examination signs for detecting aspiration in adults following stroke. J. Common. Disorder. 3455–72 (2001).

    CAS Google Scholar Article

  • Smith Hammond, California et al. Predicting aspiration in patients with ischemic stroke: comparison of clinical signs and aerodynamic measures of voluntary cough. Chest 135769-777 (2009).

    Google Scholar article

  • Warms, T. & Richards, J. “Wet Voice” as a predictor of penetration and aspiration in oropharyngeal dysphagia. Dysphagia 1584–88 (2000).

    CAS Google Scholar Article

  • Groves-Wright, KJ Acoustics and perception of wet voice quality in identifying penetration/aspiration during swallowing (University of Cincinnati, 2007).

    Google Scholar

  • Ryu, JS, Park, SR & Choi, KH Prediction of laryngeal aspiration using voice analysis. A m. J.Phys. Med. Rehabilit. 83753–757 (2004).

    Google Scholar article

  • Kang, YA, Kim, J., Jee, SJ, Jo, CW & Koo, BS Detection of voice changes due to aspiration via acoustic voice analysis. Auris Nasus Larynx 45801–806 (2018).

    Google Scholar article

  • Dankovičová, Z., Sovák, D., Drotár, P. & Vokorokos, L. Machine learning approach for dysphonia detection. Appl. Science. 81927 (2018).

    Google Scholar article

  • Ali, Z., Hossain, MS, Muhammad, G. & Sangaiah, AK A smart health system for detection and classification to discriminate vocal cord disorders. Future generator. Calculation. System 8519-28 (2018).

    Google Scholar article

  • Maor, E. et al. Voice signal characteristics are independently associated with coronary heart disease. Mayo Clin. proc. 93840–847 (2018).

    Google Scholar article

  • Sara, JDS et al. The non-invasive voice biomarker is associated with pulmonary hypertension. PLOS ONE 15e0231441 (2020).

    CAS Google Scholar Article

  • Manfredi, C. et al. Smartphones offer new opportunities in clinical voice research. J.Voice 31(111), 111-e111-112 (2017).

    Google Scholar

  • Petrizzo, D. & Popolo, PS Smartphone use in clinical voice recording and acoustic analysis: a review of the literature. J.Voice 35499e423-499e428 (2021).

    Google Scholar article

  • Fetik, E. et al. New bedside phonetic assessment to identify dysphagia and risk of aspiration. Chest 149649-659 (2016).

    Google Scholar article

  • Umayahara, Y. et al. Mobile cough strength assessment device using cough sounds. Sensors (Basel) 183810 (2018).

    Article on Google Scholar Ads

  • Kulnik, ST et al. A higher cough rate is associated with a lower risk of pneumonia in acute stroke. Thorax 71474–475 (2016).

    Google Scholar article

  • American Thoracic Society/European Respiratory, S. ATS/ERS Statement on Respiratory Muscle Testing. A m. J. Breathe. Crit. Med Care 166518–624 (2002).

    Google Scholar article

  • Park, GY et al. Decreased diaphragm excursion in stroke patients with dysphagia as assessed by M-mode ultrasound. Camber. Phys. Med. Rehabilit. 96114-121 (2015).

    Google Scholar article

  • Sohn, D. et al. Determination of cut-off values ​​for peak cough flow to predict aspiration pneumonia in patients with dysphagia using the citric acid reflex cough test. Camber. Phys. Med. Rehabilit. 992532-2539 and 2531 (2018).

    Google Scholar article

  • Fang, S.-H., Wang, C.-T., Chen, J.-Y., Tsao, Y. & Lin, F.-C. Combine acoustic signals and medical records to improve pathological voice classification. APSIPA Trans. Process signal information. 8e14 (2019).

    Google Scholar article

  • Mroueh, Y., Marcheret, E. & Goel, V. Deep multimodal learning for audiovisual speech recognition. In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2130-2134 (2015).

  • Mann, G., Hankey, GJ, and Cameron, D. Swallowing function after stroke: prognosis and prognostic factors at 6 months. Stroke 30744–748 (1999).

    CAS Google Scholar Article

  • Splaingard, ML, Hutchins, B., Sulton, LD, and Chaudhuri, G. Aspiration in rehabilitation patients: videofluoroscopy vs bedside clinical assessment. Camber. Phys. Med. Rehabilit. 69637–640 (1988).

    CAS PubMed Google Scholar

  • Henke, C., Foerch, C. & Lapa, S. Parameters for early detection of dysphagia in acute ischemic stroke. Cerebrovascular. Say. 44285-290 (2017).

    Google Scholar article

  • Jeyaseelan, RD, Vargo, MM & Chae, J. National Institutes of Health Stroke Scale (NIHSS) as an early predictor of post-stroke dysphagia. PM R seven593–598 (2015).

    Google Scholar article

  • Yu, KJ & Park, D. Clinical characteristics of dysphagic stroke patients with salivary aspiration: a retrospective STROBE-compliant study. Medicine (Baltimore) 98e14977 (2019).

    Google Scholar article

  • Han, YJ, Jang, YJ, Park, GY, Joo, YH & Im, S. Role of injection laryngoplasty in prevention of post-stroke aspiration pneumonia, case series report. Medicine (Baltimore) 9919220 (2020).

    Google Scholar article

  • Hammond, CAS & Goldstein, LB Coughing and aspiration of food and liquids due to oropharyngeal dysphagia: ACCP evidence-based clinical practice guidelines. Chest 129154S-168S (2006).

    Google Scholar article

  • McCullough, GH et al. Usefulness of swallowing clinical examination measures for detecting post-stroke aspiration. J. Speech. Lang. To listen. Res. 481280-1293 (2005).

    CAS Google Scholar Article

  • Xu, Y. et al. The extreme gradient reinforcement model performs better in predicting the risk of 90-day readmission in patients with ischemic stroke. J. Stroke Cerebrovasc. Say. 28104441 (2019).

    Google Scholar article

  • Li, X. et al. Using machine learning to predict stroke-associated pneumonia in Chinese patients with acute ischemic stroke. EUR. J. Neurol. 271656-1663 (2020).

    CAS Google Scholar Article

  • Kim, H. et al. The convolutional neural network classifies pathological voice change in laryngeal cancer with high accuracy. J. Clin. Med. 93415 (2020).

    CAS Google Scholar Article

  • Maryn, Y., Roy, N., De Bodt, M., Van Cauwenberge, P. & Corthals, P. Acoustic measurement of global voice quality: a meta-analysis. J.Acoust. Soc. A m. 1262619-2634 (2009).

    Article on Google Scholar Ads

  • Dudik, JM, Kurosu, A., Coyle, JL & Sejdic, E. Dysphagia and its effects on sound and vibration swallowing in adults. Biomedical. Eng. On line 1769 (2018).

    Google Scholar article

  • Khalifa, Y., Coyle, JL, and Sejdic, E. Noninvasive identification of swallows via deep learning in high-resolution cervical auscultation recordings. Science. representing ten8704 (2020).

    ADS CAS Article Google Scholar

  • Roldan-Vasco, S., Orozco-Duque, A., Suarez-Escudero, JC & Orozco-Arroyave, JR Machine learning-based analysis of speech dimensions in functional oropharyngeal dysphagia. Calculation. Methods Programs Biomed. 208106248 (2021).

    Google Scholar article

  • Sherry J. Basler