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Abstract Background Parkinson’s disease is one of the non-curable diseases and occurs by the prominent loss of neurotransmitter (dopamine) in substantia nigra pars compacta (SNpc). The main cause behind this is not yet identified and even its diagnosis is very intricate phase due to non-identified onset symptoms. Despite the fact that PD has been extensively researched over the decades, and various algorithms and strategies for early recognition and avoiding misdiagnosis have been published. The objective of this article is to focus on the current scenario and to explore the involvement of various clinical diagnostic scales in the detection of PD. Method An exhaustive literature review is conducted to synthesize the earlier work in this area, and the articles were searched using different keywords like Parkinson disease, motor/non-motor, treatment, diagnosis, scales, PPMI, etc., in all repositories such as Google scholar, Scopus, Elsevier, PubMed and many more. From the year 2017 to 2021, a total of 451 publications were scanned, but only 24 studies were chosen for a review process. Findings Mostly as clinical tools, UPDRS and HY scales are commonly used and even there are many other scales which can be helpful in detection of symptoms such as depression, anxiety, sleepiness, apathy, smell, anhedonia, fatigue, pain, etc., that affect the QoL of pateint. The recognition of non-motor manifests is typically very difficult than motor signs. Conclusion This study can give the beneficial research paths at an early stage diagnosis by focusing on frequent inspection of daily activities, interactions, and routine, which may also give a plethora of information on status changes, directing self-reformation, and clinical therapy.

Power spectral analysis of short-term heart rate variability (HRV) can provide instant valuable information to understand the functioning of autonomic control over the cardiovascular system. In this study, an adaptive continuous Morlet wavelet transform (ACMWT) method has been used to describe the time-frequency characteristics of the HRV using band power spectra and the median value of interquartile range. Adaptation of the method was based on the measurement of maximum energy concentration. The ACMWT has been validated on synthetic signals (i.e. stationary, non-stationary as slow varying and fast changing frequency with time) modeled as closest to dynamic changes in HRV signals. This method has been also tested in the presence of additive white Gaussian noise (AWGN) to show its robustness towards the noise. From the results of testing on synthetic signals, the ACMWT was found to be an enhanced energy concentration estimator for assessment of power spectral of short-term HRV time series compared to adaptive Stockwell transform (AST), adaptive modified Stockwell transform (AMST), standard continuous Morlet wavelet transform (CMWT) and Stockwell transform (ST) estimators at statistical significance level of 5%. Further, the ACMWT was applied to real HRV data from Fantasia and MIT-BIH databases, grouped as healthy young group (HYG), healthy elderly group (HEG), arrhythmia controlled medication group (ARCMG), and supraventricular tachycardia group (SVTG) subjects. The global results demonstrate that spectral indices of low frequency power (LFp) and high frequency power (HFp) of HRV were decreased in HEG compared to HYG subjects (p