![]() Ghosh R, Vamshi C, Kumar P (2019) Rnn based online handwritten word recognition in devanagari and bengali scripts using horizontal zoning. Ghosh R, Roy PP, Kumar P (2018) Smart device authentication based on online handwritten script identification and word recognition in indic scripts using zone-wise features. ![]() Ghosh R (2021) A recurrent neural network based deep learning model for offline signature verification and recognition system. Comput Methods Prog Biomed 117(3):405–411ĭrotár P, Mekyska J, Rektorová I, Masarová L, Smékal Z, Faundez-Zanuy M (2016) Evaluation of handwriting kinematics and pressure for differential diagnosis of parkinson’s disease. IEEE Trans Neural Syst Rehabil Eng 23(3):508–516ĭrotár P, Mekyska J, Rektorová I, Masarová L, Smékal Z, Faundez-Zanuy M (2014) Analysis of in-air movement in handwriting: A novel marker for parkinson’s disease. Expert Syst Appl 168:114405ĭrotár P, Mekyska J, Rektorová I, Masarová L, Smékal Z, Faundez-Zanuy M (2014) Decision support framework for parkinson’s disease based on novel handwriting markers. Pattern Recogn Lett 128:204–210ĭiaz M, Moetesum M, Siddiqi I, Vessio G (2021) Sequence-based dynamic handwriting analysis for parkinson’s disease detection with one-dimensional convolutions and bigrus. Mov Disord 17(4):835–837ĭiaz M, Ferrer MA, Impedovo D, Pirlo G, Vessio G (2019) Dynamically enhanced static handwriting representation for parkinson’s disease detection. Pattern Recog Lett 121:37–45ĭerkinderen P, Dupont S, Vidal JS, Chedru F, Vidailhet M (2002) Micrographia secondary to lenticular lesions. Lancet Neurol 5(6):525–535ĭe Stefano C, Fontanella F, Impedovo D, Pirlo G, di Freca AS (2019) Handwriting analysis to support neurodegenerative diseases diagnosis: A review. Mov Disord 25(S1):S76–S77ĭe Lau LML, Breteler MMB (2006) Epidemiology of parkinson’s disease. Hum Mov Sci 30(4):783–791īurke RE (2010) Evaluation of the braak staging scheme for parkinson’s disease: Introduction to a panel presentation. ![]() Neural Comput Appl pp 1–21īidet-Ildei C, Pollak P, Kandel S, Fraix V, Orliaguet JP (2011) Handwriting in patients with parkinson disease: effect of l-dopa and stimulation of the sub-thalamic nucleus on motor anticipation. Ieee Access 7:116480–116489Īlissa M, Lones MA, Cosgrove J, Alty JE, Jamieson S, Smith SL, Vallejo M (2022) Parkinson’s disease diagnosis using convolutional neural networks and figure-copying tasks. IEEE AccessĪli L, Zhu C, Golilarz NA, Javeed A, Zhou M, Liu Y (2019) Reliable parkinson’s disease detection by analyzing handwritten drawings: Construction of an unbiased cascaded learning system based on feature selection and adaptive boosting model. The experimental results show that the proposed method outperforms the state-of-the-art methods in this regard.Ībdullah SM, Abbas T, Bashir MH, Khaja IA, Ahmad M, Soliman NF, El-Shafai W (2023) Deep transfer learning based parkinson’s disease detection using optimized feature selection. The parkinson’s disease classification performance of the proposed method has been evaluated on a very popular publicly available dataset parkinsion disease handwriting database (PaHaW). Various kinematics features have been extracted from different handwritten tasks and the extracted features have been studied by three different machine learning techniques named support vector machine (SVM), adaboost classifier, and bagged random forest (BRF) as well as two different variants of deep learning model RNN known as long short-term memory (LSTM) and bi-directional long short-term memory (BLSTM). The aim of this work is twofold: (a) to find out the best task/tasks capable to discriminate between PD patient and healthy person and (b) to develop a robust method to detect the PD patient. This article proposes bi-directional long short-term memory based method to develop a parkinson’s disease diagnosis system by analyzing online handwritten tasks. Although several works exist using machine learning techniques to detect parkinson’s disease, very few have focused to detect this disease by analyzing online handwritten tasks. Early diagnosis can reduce its severity as well as the expenditure incurred for the treatment. Parkinson’s disease is an escalating neurodegenerative disorder that adversely affects movement, muscle flexibility, speech, and writing skills.
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