Addressing smartphone mismatch in Parkinson’s disease detection aid systems based on speech

Madruga, Mario; Campos-Roca, Y.; Pérez, Carlos J.

doi:10.1016/j.bspc.2022.104281

Cited by 4 publications

(1 citation statement)

References 29 publications

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“…Madruga et al [22] analyzed the impact of the variability that exists between different voice signal acquisition devices and the negative effects on voice signal recordings specifically oriented for Parkinson's recognition. They proposed a methodology to increase the robustness against the variability between different devices.…”

Section: Introductionmentioning

confidence: 99%

Parkinson’s Disease Detection from Voice Recordings Using Associative Memories

Luna-Ortiz,

Aldape-Pérez,

Uriarte-Arcia

et al. 2023

Healthcare

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Parkinson’s disease (PD) is a neurological condition that is chronic and worsens over time, which presents a challenging diagnosis. An accurate diagnosis is required to recognize PD patients from healthy individuals. Diagnosing PD at early stages can reduce the severity of this disorder and improve the patient’s living conditions. Algorithms based on associative memory (AM) have been applied in PD diagnosis using voice samples of patients with this health condition. Even though AM models have achieved competitive results in PD classification, they do not have any embedded component in the AM model that can identify and remove irrelevant features, which would consequently improve the classification performance. In this paper, we present an improvement to the smallest normalized difference associative memory (SNDAM) algorithm by means of a learning reinforcement phase that improves classification performance of SNDAM when it is applied to PD diagnosis. For the experimental phase, two datasets that have been widely applied for PD diagnosis were used. Both datasets were gathered from voice samples from healthy people and from patients who suffer from this condition at an early stage of PD. These datasets are publicly accessible in the UCI Machine Learning Repository. The efficiency of the ISNDAM model was contrasted with that of seventy other models implemented in the WEKA workbench and was compared to the performance of previous studies. A statistical significance analysis was performed to verify that the performance differences between the compared models were statistically significant. The experimental findings allow us to affirm that the proposed improvement in the SNDAM algorithm, called ISNDAM, effectively increases the classification performance compared against well-known algorithms. ISNDAM achieves a classification accuracy of 99.48%, followed by ANN Levenberg–Marquardt with 95.89% and SVM RBF kernel with 88.21%, using Dataset 1. ISNDAM achieves a classification accuracy of 99.66%, followed by SVM IMF1 with 96.54% and RF IMF1 with 94.89%, using Dataset 2. The experimental findings show that ISNDAM achieves competitive performance on both datasets and that statistical significance tests confirm that ISNDAM delivers classification performance equivalent to that of models published in previous studies.

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Section: Introductionmentioning

confidence: 99%

Parkinson’s Disease Detection from Voice Recordings Using Associative Memories

Luna-Ortiz,

Aldape-Pérez,

Uriarte-Arcia

et al. 2023

Healthcare

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Investigation of Scalograms with a Deep Feature Fusion Approach for Detection of Parkinson’s Disease

Cantürk,

Günay

2024

Cogn Comput

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Parkinson’s disease (PD) is a neurological condition that millions of people worldwide suffer from. Early symptoms include a slight sense of weakness and a propensity for involuntary tremulous motion in body limbs, particularly in the arms, hands, and head. PD is diagnosed based on motor symptoms. Additionally, scholars have proposed various remote monitoring tests that offer benefits such as early diagnosis, ease of application, and cost-effectiveness. PD patients often exhibit voice disorders. Speech signals of the patients can be used for early diagnosis of the disease. This study proposed an artificial intelligence–based approach for PD diagnosis using speech signals. Scalogram images, generated through the Continuous Wavelet Transform of the speech signals, were employed in deep learning techniques to detect PD. The scalograms were tested with various deep learning techniques. In the first part of the experiment, AlexNet, GoogleNet, ResNet50, and a majority voting-based hybrid system were used as classifiers. Secondly, a deep feature fusion method based on DenseNet and NasNet was investigated. Several evaluation metrics were employed to assess the performance. The deep feature fusion system achieved an accuracy of 0.95 and an F1 score with stratified 10-fold cross-validation, improving accuracy by 38% over the ablation study. The key contributions of this study include the investigation of scalogram images with a comprehensive analysis of deep learning models and deep feature fusion for PD detection.

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