Classification of parkinsonian and essential tremor using empirical mode decomposition and support vector machine

Ai, Lingmei; Wang, Jue; Yao, Ruoxia

doi:10.1016/j.dsp.2011.01.010

Cited by 54 publications

(30 citation statements)

References 18 publications

(20 reference statements)

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“…The results are more accurate because of their direct contact and rigid attachment with body, apart from this these wearables are lightweight and have good longevity. Distinctive calculations are implemented over wearable signals to explore, screen or perceive the Parkinson patients.The analysts [5] analyzed basic tremor and Parkinson infection utilizing particular singular value decomposition (SVD) to find highlights of intrinsic mode functions (IMFs) and SVM is proposed to recognize them. Hand quickened signals were gathered and pre-prepared by experimental mode disintegration (EMD) strategy and appropriated into various stationary IMF and contributed to SVM.…”

Section: Related Workmentioning

confidence: 99%

Design and Application of a Smart Diagnostic System for Parkinson’s Patients using Machine Learning

Channa¹,

Baqai²,

Ceylan³

2019

IJACSA

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For analysis of Parkinson illness gait disabilities detection is essential. The only motivation behind this examination is to equitably and consequently differentiate among sound subjects and the one who is forbearing the Parkinson, utilizing IOT based indicative framework. In this examination absolute, 16 distinctive force sensors being attached with the shoes of subjects which documented the Multisignal Vertical Ground Reaction Force (VGRF). Overall sensors signals utilizing 1024 window estimate around the raw signals, utilizing the Packet wavelet change (PWT) five diverse characteristics that includes entropy, energy, variance, standard deviation and waveform length were derived and support vector machine (SVM) is to recognize Parkinson patients and healthy subjects. SVM is trained on 85% of the dataset and tested on 15% dataset. Preparation accomplice relies upon 93 patients with idiopathic PD (mean age: 66.3 years; 63% men and 37% ladies), and 73 healthy controls (mean age: 66.3 years; 55% men and 45% ladies). IOT framework included all 16 sensors, from which 8 compel sensors were appended to left side foot of subject and the rest of the 8 on the right side foot. The outcomes demonstrate that fifth sensor worn on a Medial part of the dorsum of right foot highlighted by R5 gives 90.3% accuracy. Henceforth this examination gives the knowledge to utilize single wearable force sensor. Hence, this examination deduce that a solitary sensor might help in differentiation amongst Parkinson and healthy subjects.

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Section: Related Workmentioning

confidence: 99%

Design and Application of a Smart Diagnostic System for Parkinson’s Patients using Machine Learning

Channa¹,

Baqai²,

Ceylan³

2019

IJACSA

View full text Add to dashboard Cite

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“…Hence, the decomposition is adaptive. Moreover, the decomposition is nonlinear, so the EMD algorithm is useful for applications required nonlinear and adaptive signal processing [8][9][10]. However, as the sifting process is iterative and the IMFs are obtained only when the algorithm converges, no further iteration will be performed when the EMD algorithm is applied to these IMFs.…”

Section: Introductionmentioning

confidence: 99%

“…1. However, as the projection is linear and the wavelets kernel is predefined, this kind of linear and nonadaptive signal decompositions is not effective for some applications that required nonlinear and adaptive signal processing [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Nonlinear and adaptive undecimated hierarchical multiresolution analysis for real valued discrete time signals via empirical mode decomposition approach

Kuang

Yang

Ling

et al. 2015

Digital Signal Processing

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“…For example, [9] examines the feasibility of using body-worn IMU sensors for performing gait analysis, with the ultimate goal of inferring whether the wearer is a PD patient. In a similar spirit, [10] [11] and [12] make use of stand-alone accelerometer and gyroscope sensors to quantify tremor severity, while [13] explores the potential of using the IMU sensors embedded in smartphones as a viable means of monitoring and detecting tremor.…”

Section: Introductionmentioning

confidence: 99%

Detecting Parkinsonian Tremor From IMU Data Collected in-the-Wild Using Deep Multiple-Instance Learning

Παπαδόπουλος

Kyritsis

Klingelhoefer

et al. 2020

IEEE J. Biomed. Health Inform.

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Parkinson's Disease (PD) is a slowly evolving neurological disease that affects about 1% of the population above 60 years old, causing symptoms that are subtle at first, but whose intensity increases as the disease progresses. Automated detection of these symptoms could offer clues as to the early onset of the disease, thus improving the expected clinical outcomes of the patients via appropriately targeted interventions. This potential has led many researchers to develop methods that use widely available sensors to measure and quantify the presence of PD symptoms such as tremor, rigidity and braykinesia. However, most of these approaches operate under controlled settings, such as in lab or at home, thus limiting their applicability under free-living conditions. In this work, we present a method for automatically identifying tremorous episodes related to PD, based on IMU signals captured via a smartphone device. We propose a Multiple-Instance Learning approach, wherein a subject is represented as an unordered bag of accelerometer signal segments and a single, expert-provided, tremor annotation. Our method combines deep feature learning with a learnable pooling stage that is able to identify key instances within the subject bag, while still being trainable end-to-end. We validate our algorithm on a newly introduced dataset of 45 subjects, containing accelerometer signals collected entirely in-the-wild. The good classification performance obtained in the conducted experiments suggests that the proposed method can efficiently navigate the noisy environment of in-the-wild recordings.

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Classification of parkinsonian and essential tremor using empirical mode decomposition and support vector machine

Cited by 54 publications

References 18 publications

Design and Application of a Smart Diagnostic System for Parkinson’s Patients using Machine Learning

Design and Application of a Smart Diagnostic System for Parkinson’s Patients using Machine Learning

Nonlinear and adaptive undecimated hierarchical multiresolution analysis for real valued discrete time signals via empirical mode decomposition approach

Detecting Parkinsonian Tremor From IMU Data Collected in-the-Wild Using Deep Multiple-Instance Learning

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