Bruxism is a medical condition in which a person’s teeth grind or clench. If you have bruxism, you may have unconsciously clenched or ground your teeth while awake (awake bruxism) or while sleeping (sleep bruxism). Polysomnography, Overnight sleep studies at a sleep clinic are the most definitive approach to identify sleep bruxism. Polysomnography will include monitoring of the heart and muscles, as well as pulse and blood pressure data. To transmit polysomnography data via telemedicine, the best compression technique should be chosen. As a result, in this study, the dominant signal in sleep bruxism diagnosis, such as ECG and EMG, is tested under various wavelets to find the best wavelet for compression and data transmission with low error. ECG and EMG signals are tested using the MATLAB tool with various wavelet transforms such as Haar, db8, symmetrical, coiflets, Biorthogonal, and reverse bior wavelet transforms. To determine the best wavelet, for each wavelet, the experimental findings for Percent Root Mean Square Difference (PRD), Compression Ratio (CR), and Recovery Ratio are obtained.

Wavelet Based Compression Analysis For Tele-Sleep Bruxism Diagnosis

This study proposes new electrocardiographic markers for prediction of ventricular arrhythmias. Two types of sinus rhythm were studied including sinus rhythm prior to ventricular arrhythmias (Pre-VF) group and normal sinus rhythm (SN) group. The technique based on wavelet transform was used for extracting electrographic markers. Three characteristic features of wavelet spectrum including frequency mean (FM), variance (VAR), and skewness (SK) were extracted from individual electrocardiogram (ECG) beat. We found that the wavelet spectrums in SN group were significantly broader than Pre-VF group. Furthermore the wavelet spectrums in Pre-VF group were more positively skewed distribution compared to SN group. Also, the FM marker achieved the best performance in separating the Pre-VF group from the SN group with the AUC of 98.9%, the sensitivity of 93%, and the specificity of 98%. Patients in the pre-VF group can be completely separated from healthy subjects using FM and SK as co-markers.

Novel electrocardiographic markers based on wavelet transform for prediction of ventricular arrhythmias

Bruxism is a clinical syndrome characterized by teeth grinding or clenching. If you suffer from bruxism, you may have unintentionally clenched or ground your teeth while you were awake (awake bruxism) or while you were asleep (sleep bruxism). The most accurate ways to identify sleep bruxism are polysomnography and nightly sleep studies at a sleep clinic. Polysomnography will include heart and muscle monitoring, as well as pulse and blood pressure data. As a result, in this study, the key signals for diagnosing sleep bruxism-such as pulse rate, ECG (electrocardiogram), and EMG (Electromyogram) are collected and stored in IOT through wireless monitoring. The hardware tools are used to test ECG and EMG signals. If the person has a sleep bruxism episode, the microcontroller will give an alert via buzzer and transfer the data via IoT to the cloud, which can be accessed by the doctor for tele-monitoring. 

IoT Based Tele-Sleep Bruxism Diagnosis Tool

This paper discusses the recording and compression analysis of an Electrogastrogram (EGG), a non-invasive instrument that visually represents the electrical activity of the stomach to diagnose stomach illnesses. The EGG signal’s compression is important in the diagnosis, prognosis, and survival analysis of all stomach-related disorders, especially in telemedicine applications where the patient is geographically isolated. Over the years, several signal compression algorithms have been presented. High cost, signal degradation, and a low compression ratio are just a few drawbacks that result in an inefficient signal at the receiver’s end. The advantages of EGG compression in digital domain for telemedicine applications are effective utilization of storage data, reduced data transmission rate, and efficient transmission bandwidth. Various wavelet transformations such as biorthogonal, coiflet, daubechies, haar, reverse biorthogonal, and symlet wavelet transforms are applied to EGG signals and examined using MATLAB software in this paper. The wavelet’s performance was evaluated to select the best wavelet for telemedicine. This is accomplished by a quantitative analysis of the recovery ratio, percent root mean square difference (PRD), and compression ratio (CR) measurements. The findings of this study in terms of determining the optimal signal compression performance can undoubtedly become a valuable asset in the telemedicine area for the transmission of quantitative biological signals. 

Performance Evaluation of Electrogastrogram (EGG) Signal Compression for Telemedicine Using Various Wavelet Transform

Bruxism is a medical condition in which a person’s teeth grind or clench. If you have bruxism, you may have unconsciously clenched or ground your teeth while awake (awake bruxism) or while sleeping (sleep bruxism). Polysomnography, Overnight sleep studies at a sleep clinic are the most definitive approach to identify sleep bruxism. Polysomnography will include monitoring of the heart and muscles, as well as pulse and blood pressure data. To transmit polysomnography data via telemedicine, the best compression technique should be chosen. As a result, in this study, the dominant signal in sleep bruxism diagnosis, such as ECG and EMG, is tested under various wavelets to find the best wavelet for compression and data transmission with low error. ECG and EMG signals are tested using the MATLAB tool with various wavelet transforms such as Haar, db8, symmetrical, coiflets, Biorthogonal, and reverse bior wavelet transforms. To determine the best wavelet, for each wavelet, the experimental findings for Percent Root Mean Square Difference (PRD), Compression Ratio (CR), and Recovery Ratio are obtained. 

In this paper, the orthogonality of coefficient matrices of wavelet filters is utilized to derive the energy equation for the relation between time-domain signal and its corresponding wavelet coefficients. Using the energy equation, the relationship between the wavelet coefficient error and the reconstruction error is obtained. The errors considered in this paper include the truncation error and quantization error. This not only helps to control the reconstruction quality but also brings two advantages: (1) It is not necessary to perform inverse transform to obtain the distortion caused by compression using wavelet transform and can thus reduce computation efforts. (2) By using the energy equation, we can search for a threshold value to attain a better compression ratio within the range of a pre-specified percent root-mean-square difference (PRD) value. A compression algorithm with run length encoding is proposed based on the energy equation. In the end, the Matlab software and MIT-BIH database are adopted to perform simulations for verifying the feasibility of our proposed method. The algorithm is also implemented on a DSP chip to examine the practicality and suitability. The required computation time of an ECG segment is less than 0.0786 ,s which is fast enough to process real-time signals. As a result, the proposed algorithm is applicable for implementation on mobile ECG recording devices.

Chia-Jung Hsu

Papers

Design and implementation of ecg compression algorithm with controllable percent root-mean-square difference