Detection of Parkinson's disease with keystroke data.

Parkinson's disease (PD) is one of the most widespread neurological disorders associated with nerve damage without definitive treatment. Impairments, such as trembling and slowing down in hand movements are among the first symptoms. For this purpose, in this study, machine learning (ML)-based models were developed by using keyboard keystroke dynamics. According to patients' drug use status, disease severity, and gender, we created 14 different sub-datasets and extracted 378 features using raw keystroke data. We developed alternative models with Support Vector Machines (SVM), k-Nearest Neighbors (kNN), and Random Forest (RF) algorithms. We further used Minimum Redundancy Maximum Relevance (mRmR), RELIEF, sequential forward selection (SFS), and RF feature selection methods to investigate prominent features in distinguishing PD. We developed ML models that jointly use the most popular features of selection algorithms (feature ensemble [FE]) and an ensemble classifier by combining multiple ML algorithms utilizing majority vote (model ensemble [ME]). With 14 different sets, FE and ME models provided accuracy (Acc.) in the range of 91.73 - 100% and 81.08 - 100%, respectively.

MSCCov19Net: multi-branch deep learning model for COVID-19 detection from cough sounds.

Coronavirus has an impact on millions of lives and has been added to the important pandemics that continue to affect with its variants. Since it is transmitted through the respiratory tract, it has had significant effects on public health and social relations. Isolating people who are COVID positive can minimize the transmission, therefore several exams are proposed to detect the virus such as reverse transcription-polymerase chain reaction (RT-PCR), chest X-Ray, and computed tomography (CT). However, these methods suffer from either a low detection rate or high radiation dosage, along with being expensive. In this study, deep neural network-based model capable of detecting coronavirus from only coughing sound, which is fast, remotely operable and has no harmful side effects, has been proposed. The proposed multi-branch model takes M el Frequency Cepstral Coefficients (MFCC), S pectrogram, and C hromagram as inputs and is abbreviated as MSCCov19Net. The system is trained on publicly available crowdsourced datasets, and tested on two unseen (used only for testing) clinical and non-clinical datasets. Experimental outcomes represent that the proposed system outperforms the 6 popular deep learning architectures on four datasets by representing a better generalization ability. The proposed system has reached an accuracy of 61.5 % in Virufy and 90.4 % in NoCoCoDa for unseen test datasets.

Individuals with a COVID-19 history exhibit asymmetric gait patterns despite full recovery.

COVID-19 is a multisystem infectious disease affecting the body systems. Its neurologic complications include -but are not limited to headache, loss of smell, encephalitis, and cerebrovascular accidents. Even though gait analysis is an objective measure of the neuro-motor system and may provide significant information about the pathophysiology of specific diseases, no studies have investigated the gait characteristics in adults after full recovery from COVID-19. This was a cross-sectional, controlled study that included 12 individuals (mean age, 23.0 ± 4.1 years) with mild-to-moderate COVID-19 history (COVD) and 20 sedentary controls (CONT; mean age, 24.0 ± 3.6 years). Gait was evaluated using inertial sensors on a motorized treadmill. Spatial-temporal gait parameters and gait symmetry were calculated by using at least 512 consecutive steps for each participant. The effect-size analyses were utilized to interpret the impact of the results. Spatial-temporal gait characteristics were comparable between the two groups. The COVD group showed more asymmetrical gait patterns than the CONT group in the double support duration symmetry (p = 0.042), single support duration symmetry (p = 0.006), loading response duration symmetry (p = 0.042), and pre-swing duration symmetry (p = 0.018). The effect size analyses of the differences showed large effects (d = 0.68-0.831). Individuals with a history of mild-to-moderate COVID-19 showed more asymmetrical gait patterns than individuals without a disease history. Regardless of its severity, the multifaceted long-term effects of COVID-19 need to be examined and the scope of clinical follow-up should be detailed.

Resonance based separation and energy based classification of lung sounds using tunable wavelet transform.

BACKGROUND AND OBJECTIVE: The locations and occurrence pattern of adventitious sounds in the respiratory cycle have critical diagnostic information. In a lung sound sample, the crackles and wheezes may exist individually or they may coexist in a successive/overlapping manner superimposed onto the breath noise. The performance of the linear time-frequency representation based signal decomposition methods has been limited in the crackle/wheeze separation problem due to the common signal components that may arise in both time and frequency domain. However, the proposed resonance based decomposition can be used to isolate crackles and wheezes which behave oppositely in time domain even if they share common frequency bands. METHODS: In the proposed study, crackle and/or wheeze containing synthetic and recorded lung-sound signals were decomposed by using the resonance information which is produced by joint application of the Tunable Q-factor Wavelet Transform and Morphological Component Analysis. The crackle localization and signal reconstruction performance of the proposed approach was compared with the previously suggested Independent Component Analysis and Empirical Mode Decomposition methods in a quantitative and qualitative manner. Additionally, the decomposition ability of the proposed approach was also used to discriminate crackle and wheeze waveforms in an unsupervised way by employing signal energy. RESULTS: Results have shown that the proposed approach has significant superiority over its competitors in terms of the crackle localization and signal reconstruction ability. Moreover, the calculated energy values have revealed that the transient crackles and rhythmic wheezes can be successfully decomposed into low and high resonance channels by preserving the discriminative information. CONCLUSIONS: It is concluded that previous works suffer from deforming the waveform of the crackles whose time domain parameters are vital in computerized diagnostic classification systems. Therefore, a method should provide automatic and simultaneous decomposition ability, with smaller root mean square error and higher accuracy as demonstrated by the proposed approach.

An open access database for the evaluation of respiratory sound classification algorithms.

OBJECTIVE: Over the last few decades, there has been significant interest in the automatic analysis of respiratory sounds. However, currently there are no publicly available large databases with which new algorithms can be evaluated and compared. Further developments in the field are dependent on the creation of such databases. APPROACH: This paper describes a public respiratory sound database, which was compiled for an international competition, the first scientific challenge of the IFMBE's International Conference on Biomedical and Health Informatics. The database includes 920 recordings acquired from 126 participants and two sets of annotations. One set contains 6898 annotated respiratory cycles, some including crackles, wheezes, or a combination of both, and some with no adventitious respiratory sounds. In the other set, precise locations of 10 775 events of crackles and wheezes were annotated. MAIN RESULTS: The best system that participated in the challenge achieved an average score of 52.5% with the respiratory cycle annotations and an average score of 91.2% with the event annotations. SIGNIFICANCE: The creation and public release of this database will be useful to the research community and could bring attention to the respiratory sound classification problem.

Wheeze type classification using non-dyadic wavelet transform based optimal energy ratio technique.

BACKGROUND AND OBJECTIVE: Wheezes in pulmonary sounds are anomalies which are often associated with obstructive type of lung diseases. The previous works on wheeze-type classification focused mainly on using fixed time-frequency/scale resolution based on Fourier and wavelet transforms. The main contribution of the proposed method, in which the time-scale resolution can be tuned according to the signal of interest, is to discriminate monophonic and polyphonic wheezes with higher accuracy than previously suggested time and time-frequency/scale based methods. METHODS: An optimal Rational Dilation Wavelet Transform (RADWT) based peak energy ratio (PER) parameter selection method is proposed to discriminate wheeze types. Previously suggested Quartile Frequency Ratios, Mean Crossing Irregularity, Multiple Signal Classification, Mel-frequency Cepstrum and Dyadic Discrete Wavelet Transform approaches are also applied and the superiority of the proposed method is demonstrated in leave-one-out (LOO) and leave-one-subject-out (LOSO) cross validation schemes with support vector machine (SVM), k nearest neighbor (k-NN) and extreme learning machine (ELM) classifiers. RESULTS: The results show that the proposed RADWT based method outperforms the state-of-the-art time, frequency, time-frequency and time-scale domain approaches for all classifiers in both LOO and LOSO cross validation settings. The highest accuracy values are obtained as 86% and 82.9% in LOO and LOSO respectively when the proposed PER features are fed into SVM. CONCLUSIONS: It is concluded that time and frequency domain characteristics of wheezes are not steady and hence, tunable time-scale representations are more successful in discriminating polyphonic and monophonic wheezes when compared with conventional fixed resolution representations.

Performance comparison of wavelet based denoising methods on discontinuous adventitious lung sounds.

Crackles and their time-domain characteristics provide important clues about different lung diseases. In this paper, we aim to de-noise synthetically produced crackles under various noise levels while preserving their information bearing parts which significantly affect crackle parameters. Classical wavelet based de-noising algorithms are deteriorated by sharp-sudden noise changes and produce Gibbs like fluctuations. On the other hand, total variation based algorithms, which are capable of alleviating the drawbacks of the classical wavelet based algorithms, are failed when dealing with piecewise-smooth signals like crackles and generate unwanted flat regions on the de-noised signals. Proposed wavelet total variation based de-noising is succeed in removing undesired artefacts originating from both classical wavelet and total variation de-noising. The proposed method is compared with classical wavelet based de-noising methods in terms of root mean square error under various white Gaussian noise levels (0 - 20 dB SNR). Moreover, in order to emphasize the de-noising ability of the methods, without deforming crackle waveform, time and frequency domain representation of a noisy and de-noised crackle is validated visually.

Resonance based respiratory sound decomposition aiming at localization of crackles in noisy measurements.

In this work, resonance based decomposition of lung sounds that aims to separate wheeze, crackle and vesicular sounds into three individual channels while automatically localizing crackles for both synthetic and real data is presented. Previous works focus on stationary-non stationary discrimination to separate crackles and vesicular sounds disregarding wheezes which are stationary as compared to crackles. However, wheeze sounds include important cues about the underlying pathology. Using two different threshold methods and synthetic sound generation scenarios in the presence of wheezes, resonance based decomposition performs 89.5 % crackle localization recall rate for white Gaussian noise and 98.6 % crackle localization recall rate for healthy vesicular sound treated as noise at low signal-to-noise ratios. Besides, an adaptive threshold determination which is independent from the channel at which it will be applied is used and is found to be robust to noise.

A lung sound classification system based on the rational dilation wavelet transform.

In this work, a wavelet based classification system that aims to discriminate crackle, normal and wheeze lung sounds is presented. While the previous works related with this problem use constant low Q-factor wavelets, which have limited frequency resolution and can not cope with oscillatory signals, in the proposed system, the Rational Dilation Wavelet Transform, whose Q-factors can be tuned, is employed. Proposed system yields an accuracy of 95 % for crackle, 97 % for wheeze, 93.50 % for normal and 95.17 % for total sound signal types using energy feature subset and proposed approach is superior to conventional low Q-factor wavelet analysis.

Feature extraction using time-frequency analysis for monophonic-polyphonic wheeze discrimination.

The aim of this study is monophonic-polyphonic wheeze episode discrimination rather than the conventional wheeze (versus non-wheeze) episode detection. We used two different methods for feature extraction to discriminate monophonic and polyphonic wheeze episodes. One of the methods is based on frequency analysis and the other is based on time analysis. Frequency analysis based method uses ratios of quartile frequencies to exploit the difference in the power spectrum. Time analysis based method uses mean crossing irregularity to exploit the difference in periodicity in the time domain. Both methods are applied on the data before and after an image processing based preprocessing step. Calculated features are used in classification both individually and in combinations. Support vector machine, k-nearest neighbor and Naive Bayesian classifiers are adopted in leave-one-out scheme. A total of 121 monophonic and 110 polyphonic wheeze episodes are used in the experiments, where the best classification performances are 71.45% for time domain based features, 68.43% for frequency domain based features, and 75.78% for a combination of selected best features.