Onset Detection Algorithm Research Articles

The assessment method of lip closure ability based on sEMG nonlinear onset detection algorithms.

To overcome the limitations of traditional diagnosis of orbicularis oris muscle function in mouth-breathing patients, this study aims to propose a surface electromyographic (sEMG) based method for reliable and accurate quantitative assessment of lip closure ability. A total of 21 volunteers (16 patients and 5 healthy subjects, aged 8-16) were included in the study. Three nonlinear onset detection algorithms- Teager-Kaiser Energy (TKE) operator, Sample Entropy (SampEn), and Fuzzy Entropy (FuzzyEn)- were compared for their ability to identify lip closure in sEMG signals. Lip Closure EMG Activity Index (LCEAI) was proposed based on the action segments detected by the best performing algorithm for the quantitative assessment of lip closure. The results indicated that FuzzyEn had the highest lip closure identification rate at 93.78 %, the lowest average onset delay of 47.50 ms, the lowest average endpoint delay of 73.10 ms, and the minimal time error of 111.61 ms, exhibiting superior performance. The calculation results of the LCEAI closely corresponded with the actual degree of lip closure in patients. The lip closure ability assessment method proposed in this study can provide a quantitative basis for the diagnosis of mouth breathing.

Changes in Music Curriculum Education in Colleges and Universities Based on Cloud Classroom Assistive Technology

Abstract With the rapid development of science and technology, cloud classroom teaching has gradually become a trend. The cloud-based music teaching aid training system designed in this paper learns to map rhythmic and pitch features through music sequence generation technology. The onset detection algorithm of spectral flux detects the onset of the note. The Bezier curve is introduced into the melodic line feature generation algorithm to construct a polynomial function for interpolation between the first and the last endpoints, and the emotion is utilized to guide fractal music creation. Finally, the effectiveness of this auxiliary teaching system in college music teaching has been verified. The results show that the optimal Hamming distance of the system is 0.353 in the music melody generation experiment, which indicates that the system can quickly generate melody lines for students in music teaching and improve their learning efficiency. The system can significantly improve the rationality and efficiency of music skill training, which is worth promoting.

Parkinson’s disease tremor prediction using EEG data analysis-A preliminary and feasibility study

PurposeTremor is one of the hallmarks of Parkinson’s disease (PD) that does not respond effectively to conventional medications. In this regard, as a complementary solution, methods such as deep brain stimulation have been proposed. To apply the intervention with minimal side effects, it is necessary to predict tremor initiation. The purpose of the current study was to propose a novel methodology for predicting resting tremors using analysis of EEG time-series.MethodsA modified algorithm for tremor onset detection from accelerometer data was proposed. Furthermore, a machine learning methodology for predicting PD hand tremors from EEG time-series was proposed. The most discriminative features extracted from EEG data based on statistical analyses and post-hoc tests were used to train the classifier for distinguishing pre-tremor conditions.ResultsStatistical analyses with post-hoc tests showed that features such as form factor and statistical features were the most discriminative features. Furthermore, limited numbers of EEG channels (F3, F7, P4, CP2, FC6, and C4) and EEG bands (Delta and Gamma) were sufficient for an accurate tremor prediction based on EEG data. Based on the selected feature set, a KNN classifier obtained the best pre-tremor prediction performance with an accuracy of 73.67%.ConclusionThis feasibility study was the first attempt to show the predicting ability of EEG time-series for PD hand tremor prediction. Considering the limitations of this study, future research with longer data, and different brain dynamics are needed for clinical applications.

Onset and Offset Detection of Respiratory EMG Data Based on Energy Operator Signal.

Onset and offset detection of electromyography (EMG) data is an important step in respiratory muscle coordination assessment. Impaired respiratory coordination can indicate breathing disorders and lung diseases. In this paper, we present an algorithm for onset and offset timing detection of real-world EMG signals from respiratory muscles, which are contaminated with electrocardiogram (ECG) artifacts. The algorithm is based on the Energy Operator signal, has a low computational cost, and includes a filtering procedure to remove ECG artifacts from EMG. Analysis of EMG signals from 2 respiratory muscles of 5 participants' data shows high agreement between the algorithm and manual method with a mean difference between two methods of 0.0407 seconds.

Novel slope-based onset detection algorithm for electromyographical signals

Electromyography (EMG) is a technique of acquiring neuromuscular activity of muscle. Onset and offset gives information about activation and deactivation timings of motor units. This paper proposes a novel slope-based algorithm for onset and offset detection. EMG data are collected from different muscle of different subjects using surface EMG electrodes. Data is divided into smaller windows and average instantaneous amplitude (AIA) and slope is calculated for each window. A threshold is decided to avoid baseline noise. Below threshold, maximum and minimum slope is detected as the onset and offset respectively. The results are accurate compared to single threshold and double threshold method. Accuracy increases with computation complexity (arithmetic calculations); if compared with root mean square (RMS)-based algorithm. The only limitation is decrease in accuracy if signal is acquired between two muscle contractions. The proposed slope-based onset detection algorithm can be way out between accuracy and computational complexity.

Novel slope-based onset detection algorithm for electromyographical signals

Electromyography (EMG) is a technique of acquiring neuromuscular activity of muscle. Onset and offset gives information about activation and deactivation timings of motor units. This paper proposes a novel slope-based algorithm for onset and offset detection. EMG data are collected from different muscle of different subjects using surface EMG electrodes. Data is divided into smaller windows and average instantaneous amplitude (AIA) and slope is calculated for each window. A threshold is decided to avoid baseline noise. Below threshold, maximum and minimum slope is detected as the onset and offset respectively. The results are accurate compared to single threshold and double threshold method. Accuracy increases with computation complexity (arithmetic calculations); if compared with root mean square (RMS)-based algorithm. The only limitation is decrease in accuracy if signal is acquired between two muscle contractions. The proposed slope-based onset detection algorithm can be way out between accuracy and computational complexity.

Automatic Onset Detection of Rapid Eye Movements in REM Sleep EEG Data

Abstract Rapid eye movements (REM) during sleep is a descriptor of the REM sleep stage. Parameters associated with REM sleep, such as REM sleep numbers, REM density, REM latency, and pre-REM negativity have been associated with the functional role of REM sleep. The temporal properties of these parameters appear to play an essential role in REM sleep, so precise knowledge of these temporal properties, particularly REM onset, can help elucidate the temporal dynamics of neural activity related to REM sleep. However, manual detection of this event is a time-consuming and subjective process that can be facilitated by an automatic detection tool. We developed an automatic REM onset detection algorithm based on features describing rapid eye movements and compared the results obtained with human detection by a sleep expert.

Computational phonogram archiving

A general approach to analyzing audio files makes it possible to re-create the sound of ancient instruments, identify cross-cultural musical properties, and more.

Event Detection of Muscle Activation Using an Electromyogram

In this study, we proposed a precise onset and offset detection algorithm for muscle activation by using an electromyogram (EMG). The adaptive threshold was determined using the constant false alarm rate algorithm. The EMG signal was refined by morphological hole filling, which is used to close up and fill out missing information. By exploiting the EMG amplitude ratio in two channels, we significantly improved the offset detection performance. The proposed method does not require a training process, unlike conventional methods. The experimental results indicated that the estimated errors for both the onset and offset detection are lower than those obtained using two of the conventional methods.

Evaluating the ALERT algorithm for local outbreak onset detection in seasonal infectious disease surveillance data.

Estimation of epidemic onset timing is an important component of controlling the spread of seasonal infectious diseases within community healthcare sites. The Above Local Elevated Respiratory Illness Threshold (ALERT) algorithm uses a threshold‐based approach to suggest incidence levels that historically have indicated the transition from endemic to epidemic activity. In this paper, we present the first detailed overview of the computational approach underlying the algorithm. In the motivating example section, we evaluate the performance of ALERT in determining the onset of increased respiratory virus incidence using laboratory testing data from the Children's Hospital of Colorado. At a threshold of 10 cases per week, ALERT‐selected intervention periods performed better than the observed hospital site periods (2004/2005–2012/2013) and a CUSUM method. Additional simulation studies show how data properties may effect ALERT performance on novel data. We found that the conditions under which ALERT showed ideal performance generally included high seasonality and low off‐season incidence.

Accuracy and Reliability of Onset Detection Algorithms in Gait Initiation for Healthy Controls and Participants With Parkinson's Disease.

Accurate and reliable detection of the onset of gait initiation is essential for the correct assessment of gait. Thus, this study was aimed at evaluation of the reliability and accuracy of 3 different center of pressure-based gait onset detection algorithms: A displacement baseline-based algorithm (method 1), a velocity baseline-based algorithm (method 2), and a velocity extrema-based algorithm (method 3). The center of pressure signal was obtained during 10 gait initiation trials from 16 healthy participants and 3 participants with Parkinson's disease. Intrasession and absolute reliability of each algorithm was assessed using the intraclass correlation coefficient and the coefficient of variation of center of pressure displacement during the postural phase of gait initiation. The accuracy was evaluated using the time error of the detected onset by each algorithm relative to that of visual inspection. The authors' results revealed that although all 3 algorithms had high to very high intrasession reliabilities in both healthy subjects and subjects with Parkinson's disease, methods 2 and 3 showed significantly better absolute reliability than method 1 in healthy controls (P = .001). Furthermore, method 2 outperformed the other 2 algorithms in both healthy subjects and subjects with Parkinson's disease with an overall accuracy of 0.80. Based on these results, the authors recommend using method 2 for accurate and reliable gait onset detection.

A discussion on the paper “Determination of mortar setting times using shear wave velocity evolution curves measured by the bender element technique”

In a recent paper, Zhu et al. used bender element-type ultrasonic actuator in order to generate S-waves transmission through mortar samples. New criteria for determining the initial and final setting are proposed. The developed methodology leads to the conclusion that these criteria are more reliable than previously published material. Several points would require further discussion, such as the consideration of the effect of temperature, the shear wave onset detection algorithm, or the curve fitting strategy. These considerations might undermine the reliability of the suggested method, and explain the divergence of results obtained in contrast with previously published material.

Classification of COPD and normal lung airways using feature extraction of electromyographic signals

Airway obstruction is a common component in Chronic Obstructive Pulmonary Disease (COPD). Detection of obstruction and its grading is very essential. Obstruction in the airways, forces the accessory muscles like sternomastoid muscle (SMM) of respiration to work. Normally, only essential muscles of respiration work. In the said paper electromyographic (EMG) analysis of SMM is done for COPD and Normal subjects. We have developed improved slope based onset detection algorithm to detect the onset and offset timing of EMG. Time domain features are extracted for COPD and normal subject. The onset detection algorithm reduces the number of computations by 32.96% and increases accuracy of feature calculation by 40.19%. Dominant time domain features are selected and applied to Support Vector Machine Classifier. The SVM classification algorithm is compared with Threshold and Naïve Bayes classification algorithm. SVM gives the highest accuracy of 87.80%, sensitivity of 89.65% and specificity of 83.33%. Results are also compared with previously used FEV1/FEV6 and Forced Oscillation Technique. The activity of SMM has a significant role in the classification of Normal and COPD subject. Further analysis of SMM can be done to find different grades of COPD.

Real-time myoelectric control for a lower-limb assistive exoskeleton

This paper describes the approach to a novel algorithm for real-time myoelectric onset detection based on the classic moving average method with added features to provide onset detection while myoelectric signals are recorded from two muscles at a time. The aim of this algorithm is focused on the generation of real-time binary signals to drive a dc motor attached to the hip joint of a rehabilitation exoskeleton for the assistance on lower-limb rehabilitation of hemiplegic patients in supine/prone, seated and standing positions. With this low computational cost real-time control algorithm based on myoelectric signals, patients involved on a physical rehabilitation program wearing the developed exoskeleton will have full control over the assistance for the lower-limb exercises. The parameter tuning for the myoelectric onset detection algorithm and the results of its accuracy along several tests over two test subjects on the rectus femoris and semitendinosus muscles are presented.

EMG ONSET DETECTION - A HIDDEN FACTOR

Electromyographic (EMG) examination has gained popularity and was deployed in both practical and research fields. This technology enables one to observe the voltage generated by a neuromuscular system and to interpret body movements. Over time, it has been proved highly difficult to precisely determine the beginning of a muscle contraction. A relatively low signal to noise ratio, which is even smaller in case of a surface electromyography (sEMG), complicates this task, and more advanced onset detection algorithms were proposed. Despite the existence of statistically advanced algorithms, proposed in recent years, in many cases a manual onset detection performed by an EMG specialist remains in use, mainly due to lack of reliable solutions. This paper presents a hidden factor, not found in the literature until now, which directly relates subsequent voltage values with muscle activity. Observation of this parameter helps detect muscle onset precisely. Combined with other statistical factors, it can shed light on a totally new, enhanced branch of muscle onset estimators. This study conducts a numerical comparison to follow the hypothesis that the examined factor is correlated with a muscle contraction state.

A Review of The Dynamical Systems Modeling of Epileptic Seizures For Onset Prediction

In this review, we present a critical appraisal of works that consider epilepsy as a dynamic disease and therefore presentable from the perspective of dynamic system theory. Epilepsy is an acute brain disorder characterized by recurrent seizures where parts of the brain elicit abnormally synchronous electrical activity. The most commonly encountered forms of epilepsy are generalized absence epilepsy and temporal lobe epilepsy. The electroencephalography (EEG) which is the recording of the fluctuating electric field of the brain is the major clinical diagnostic tool for epilepsy and also a vital source of data for epilepsy research. In majority of cases accurate diagnosis of the disease can be made and seizures are controlled by the regular use of anti-epileptic drugs (AEDs). However, approximately 30% of epileptic patients suffer from medically refractory epilepsy which has defied all existing treatment protocols. Understanding the mechanisms underlying these forms of epileptic seizures and the development of alternative effective treatment strategies is a fundamental challenge in modern epilepsy research. Experimental researches show that the mechanisms involved in refractory epilepsy are so diverse and complex that it is a formidable task to obtain a single framework that categorizes all the pathophysiological changes in the properties of the epileptic brain involved. There has evolved massive evidence that seizures do not occur abruptly as it has been earlier thought but develop over time even hours before the clinical symptoms, thus raising the hope for predictability of epileptic seizure occurrence. Thus, models of the epileptic brain can be postulated using concepts from deterministic and nondeterministic dynamical systems modelling. The main idea is that since the epileptic brain transitions into and out of seizures we can view it as a dynamical system. The deterministic and non-deterministic models are based on seizure onset detection algorithm for the design of a closed loop seizure warning/intervention system. The major focus being the stimulation of the epileptic brain by sending electrical pulses to it in order to disrupt seizure progression once its onset has been detected. Finally, we considered the essential issues in epileptic seizure prediction including the sceptism expressed in recent publications on the validity of nonlinear dynamical systems modelling to epileptic seizure prediction.

Monophonic Piano Music Transcription

This paper proposes a method for computational monophonic piano music transcription, which detects the pitches of piano music and thus to identify the corresponding musical notations. This computational music transcription method consists of two main algorithms, which are Onset Detection Algorithm and Pitch Detection Algorithm. The Onset Detection Algorithm involves sound wave filtering and sound wave segmentation. And the Pitch Detection Algorithm involves period determination, frequency computation and musical notation identification. These proposed algorithms adopt time-domain method and they are built based on the observation of characteristics of piano sound signal. The program is fast and simple to use, and able to output result with 88% accuracy. However, this music transcription method is limited to specific sound input only, that is the monophonic piano music with slow or average speed up to 120 crotchet beats per minute. It is because the performances of the algorithms are dependent on the threshold values set in the program. Therefore, further investigation and research have to be carried out in order to improve the performance of the program.

Design and Implementation of an On-Chip Patient-Specific Closed-Loop Seizure Onset and Termination Detection System.

This paper presents the design of an area- and energy-efficient closed-loop machine learning-based patient-specific seizure onset and termination detection algorithm, and its on-chip hardware implementation. Application- and scenario-based tradeoffs are compared and reviewed for seizure detection and suppression algorithm and system which comprises electroencephalography (EEG) data acquisition, feature extraction, classification, and stimulation. Support vector machine achieves a good tradeoff among power, area, patient specificity, latency, and classification accuracy for long-term monitoring of patients with limited training seizure patterns. Design challenges of EEG data acquisition on a multichannel wearable environment for a patch-type sensor are also discussed in detail. Dual-detector architecture incorporates two area-efficient linear support vector machine classifiers along with a weight-and-average algorithm to target high sensitivity and good specificity at once. On-chip implementation issues for a patient-specific transcranial electrical stimulation are also discussed. The system design is verified using CHB-MIT EEG database [1] with a comprehensive measurement criteria which achieves high sensitivity and specificity of 95.1% and 96.2%, respectively, with a small latency of 1s. It also achieves seizure onset and termination detection delay of 2.98 and 3.82s, respectively, with seizure length estimation error of 4.07 s.

A new algorithm for epilepsy seizure onset detection and spread estimation from EEG signals

Appropriate diagnosis and treatment of epilepsy is a main public health issue. Patients suffering from this disease often exhibit different physical characterizations, which result from the synchronous and excessive discharge of a group of neurons in the cerebral cortex. Extracting this information using EEG signals is an important problem in biomedical signal processing. In this work we propose a new algorithm for seizure onset detection and spread estimation in epilepsy patients. The algorithm is based on a multilevel 1-D wavelet decomposition that captures the physiological brain frequency signals coupled with a generalized gaussian model. Preliminary experiments with signals from 30 epilepsy crisis and 11 subjects, suggest that the proposed methodology is a powerful tool for detecting the onset of epilepsy seizures with his spread across the brain.

Event-based Multitrack Alignment using a Probabilistic Framework

This paper presents a Bayesian probabilistic framework for real-time alignment of a recording or score with a live performance using an event-based approach. Multitrack audio files are processed using existing onset detection and harmonic analysis algorithms to create a representation of a musical performance as a sequence of time-stamped events. We propose the use of distributions for the position and relative speed which are sequentially updated in real-time according to Bayes’ theorem. We develop the methodology for this approach by describing its application in the case of matching a single MIDI track and then extend this to the case of multitrack recordings. An evaluation is presented that contrasts ourmultitrack alignment method with state-of-the-art alignment techniques.