Detection of early Parkinson’s disease with wavelet features using finger typing movements on a keyboard

Abstract

This study presents a new Parkinson's disease diagnosis technique based on wavelets extracted features and machine learning paradigms. The present-day diagnosis techniques suffer from low diagnosis accuracy and also require the patient to go to a medical facility, where the diagnosis is done by a specialist. In this work, we propose an automatic diagnosis method where by, all the patient has to do is to type some keys on their keyboard, and the algorithm will calculate the latency time, flight time and hold time of each key pressed, to make a diagnosis of Parkinson's disease. We use several wavelets to extract some features that are classified into Parkinson's disease or non-Parkinson's disease. The results are very encouraging and we obtain a classification accuracy of up to 100% in some of the cases, using a tenfold crossvalidation technique. Wavelets are a tool that can be used to complement and improve the detection of Parkinson's disease. These results will permit the amelioration of some state-of-the-art methods which use a similar technique to detect Parkinson's disease.

Technology for monitoring tremor in the upper extremities of patients with Parkinson's disease: a systematic review and a proposal

“…En esta revisión literaria se ha observado que para analizar las señales obtenidas por los sensores existen diversas metodologías. Las más utilizadas son aquellas relacionadas con técnicas de inteligencia artificial (IA) y otras como el dominio de la frecuencia y el tiempo como las implementadas en los estudios [3], [12], [15], [20], [23], [26]. En estos trabajos se utilizaron técnicas como el análisis espectral, las transformadas rápidas de Fourier, las transformadas de Hilbert, transformada de wavelets, el espectro de la potencia, la densidad espectral y diversos algoritmos de clasificación y predicción, entre otras para visualizar e identificar la frecuencia que se deseaba obtener.…”

“…Este estudio demostró una alta precisión del temblor del paciente en correlación con las anotaciones realizadas por el neurólogo utilizando la escala. Otro ejemplo es el estudio en [16], donde utilizaron el dispositivo portátil MOX5 que permite trabajar con los datos sin procesar y brinda los datos en formato European Data Formant (EDF) [26], permitiendo demostrar la fiabilidad de los estados de apagado de manera objetiva.…”

Modified SqueezeNet Architecture for Parkinson’s Disease Detection Based on Keypress Data

“…A new technique to detect PD based on wavelets-extracted features and machine learning paradigms was presented in [ 16 ]. To achieve the aimed goal, the volunteers, Parkinson’s patients and healthy individuals, were requested to perform typing tasks, from which the flight time and hold time of each pressed key were used to generate the used dataset.…”

“…The motor symptoms characteristic of the disease affect simple daily activities performed by patients, such as typing on a computer’s keyboard [ 14 , 15 ]. In view of that, and aligned to the advances in AI-based solutions and current computational power, researchers [ 16 , 17 , 18 ] are aiming to find new approaches capable of detecting the motor impairments characteristic of PD [ 19 ].…”

Preventing Digital Overdiagnosis

“…Digital screening involves the application of data analysis techniques to routinely collected data by individuals, with the goal of detecting abnormal patterns that might be compatible with the disease of interest or else might predict future disease. Examples include early detection of Parkinson disease by using information from an individual’s typing patterns on a keyboard or detection of atrial fibrillation in the general population by using smartwatches …”