Apkinson — A Mobile Monitoring Solution for Parkinson’s Disease

Klumpp, Philipp; Janu, Thomas; Arias-Vergara, Tomás; Vásquez-Correa, Juan Camilo; Orozco-Arroyave, Juan Rafael; Nöth, Elmar

doi:10.21437/interspeech.2017-416

Cited by 17 publications

(14 citation statements)

References 12 publications

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“…They computed cosine difference that confers for disease detection contributing 78% accuracy using articulation features. Klumpp et al [24] developed a model that considered the voice signal during phone calls and also considered syllable \pa-ta-ka\. They evaluated the severity and onset of PD disease in their work.…”

Section: A Machine Learning Based Methodsmentioning

confidence: 99%

A Spectrogram-Based Deep Feature Assisted Computer-Aided Diagnostic System for Parkinson’s Disease

et al. 2020

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Parkinson's disease is a neural degenerative disease. It slowly progresses from mild to severe stage, resulting in the degeneration of dopamine cells of neurons. Due to the deficiency of dopamine cells in the brain, it leads to a motor (tremor, slowness, impaired posture) and non-motor (speech, olfactory) defects in the body. Early detection of Parkinson's disease is a difficult chore as the symptoms of disease appear overtime. However, different diagnostic systems have contributed towards disease detection by considering gait, tremor and speech characteristics. Recent work has shown that speech impairments can be considered as a possible predictor for Parkinson's disease classification and remains an open research area. The speech signals show major differences and variations for Parkinson patients as compared to normal human beings. Therefore, variation in speech should be modeled using acoustic features to identify these variations. In this research, we propose three methods-the first method employs a transfer learning-based approach using spectrograms of speech recordings, the second method evaluates deep features extracted from speech spectrograms using machine learning classifiers and the third method evaluates simple acoustic feature of recordings using machine learning classifiers. The proposed frameworks are evaluated on a Spanish dataset pc-Gita. The results show that the second framework shows promising results with deep features. The highest 99.7% accuracy on vowel \o\ and read text is observed using a multilayer perceptron. Whereas 99.1% accuracy observed on vowel \i\ deep features using random forest. The deep feature-based method performs better as compared to simple acoustic features and transfer learning approaches. The proposed methodology outperforms the existing techniques on the pc-Gita dataset for Parkinson's disease detection.

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Section: A Machine Learning Based Methodsmentioning

confidence: 99%

A Spectrogram-Based Deep Feature Assisted Computer-Aided Diagnostic System for Parkinson’s Disease

et al. 2020

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“…The speech of 17 PD patients (9 male, 8 female) was recorded using the Apkinson mobile application [20]. The participants were asked to make a phone call and sustain an spontaneous conversation.…”

Section: Test Datamentioning

confidence: 99%

Unobtrusive Monitoring of Speech Impairments of Parkinson'S Disease Patients Through Mobile Devices

Arias-Vergara

Vásquez-Correa

Orozco-Arroyave

et al. 2018

2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Parkinson's disease (PD) produces several speech impairments in the patients. Automatic classification of PD patients is performed considering speech recordings collected in noncontrolled acoustic conditions during normal phone calls in a unobtrusive way. A speech enhancement algorithm is applied to improve the quality of the signals. Two different classification approaches are considered: the classification of PD patients and healthy speakers and a multi-class experiment to classify patients in several stages of the disease. According to the results it is possible to classify PD patients and healthy controls with a AUC of up to 0.87. This work is a step forward to the development of telemonitoring systems to assess the speech of the patients.

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“…One of the important advantages of the objective detection is that it can be computed solely from the speech signal and performed remotely away from hospital, which helps patients to avoid frequent visits to hospital for medical examination [12]. Pathological voice detection methods can be readily integrated into on-time screening and remote health monitoring applications [13], [14].…”

Section: Introductionmentioning

confidence: 99%

Glottal Source Information for Pathological Voice Detection

Narendra

Alku

2020

IEEE Access

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Automatic methods for the detection of pathological voice from healthy speech can be considered as potential clinical tools for medical treatment. This study investigates the effectiveness of glottal source information in the detection of pathological voice by comparing the classical pipeline approach to the end-to-end approach. The traditional pipeline approach consists of a feature extractor and a separate classifier. In the former, two sets of glottal features (computed using the quasi-closed phase glottal inverse filtering method) are used together with the widely used openSMILE features. Using both the glottal and openSMILE features extracted from voice utterances and the corresponding healthy/pathology labels, support vector machine (SVM) classifiers are trained. In building end-to-end systems, both raw speech signals and raw glottal flow waveforms are used to train two deep learning architectures: (1) a combination of convolutional neural network (CNN) and multilayer perceptron (MLP), and (2) a combination of CNN and long short-term memory (LSTM) network. Experiments were carried out using three publicly available databases, including dysarthric (the UA-Speech database and the TORGO database) and dysphonic voices (the UPM database). The performance analysis of the detection system based on the traditional pipeline approach showed best results when the glottal features were combined with the baseline openSMILE features. The results of the end-to-end approach indicated higher accuracies (about 2-3 % improvement in all three databases) when glottal flow was used as the raw time-domain input (87.93 % for UA-Speech, 81.12 % for TORGO and 76.66 % for UPM) compared to using raw speech waveform (85.12 % for UA-Speech, 78.83 % for TORGO and 73.71 % for UPM). The evaluation of both approaches demonstrate that automatic detection of pathological voice from healthy speech benefits from using glottal source information. INDEX TERMS Pathological voice, glottal source waveform, glottal features, support vector machines, end-to-end systems.

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Apkinson — A Mobile Monitoring Solution for Parkinson’s Disease

Cited by 17 publications

References 12 publications

A Spectrogram-Based Deep Feature Assisted Computer-Aided Diagnostic System for Parkinson’s Disease

A Spectrogram-Based Deep Feature Assisted Computer-Aided Diagnostic System for Parkinson’s Disease

Unobtrusive Monitoring of Speech Impairments of Parkinson'S Disease Patients Through Mobile Devices

Glottal Source Information for Pathological Voice Detection

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