Development of ECG and EMG platform with IMU to eliminate the motion artifacts found in measurements

The long term measurement and analysis of electrophysiological parameters is crucial for diagnosis of chronic diseases, and to monitor critical health parameters. It is also very important to monitor physical fitness improvement, or degradation level, of human beings where physical fitness is entirely critical for their work, or of more vulnerable members of society such as senior citizens and the sick. The state-of-the-art technological developments are leading to the use of artificial intelligence in the continuous monitoring and identification of life-threatening events in the daily life of ordinary people. However, these ambulatory measurements of electrophysiological parameters leads to drastic motion artifacts because of the test subject’s movements. Therefore, there is a dire need for the development of both hardware and software solutions to address this challenge. The scope of this thesis is to develop a hardware platform, by using off-the-shelf discrete and IC electronic components, to measure two electrophysiological parameters, electrocardiogram (ECG) and electromyogram (EMG), with an additional motion sensor inertial measurement unit (IMU) comprising nine degrees of freedom. The ECG, EMG and IMU data will be collected using the developed measurement platform from various predefined day-to-day routine activity events. A Bluetooth interface will be developed to transmit the data wirelessly, and record it on a laptop for further real-time processing. The resources of the electrical workshop and measurement lab at Aalto University will be used for the development, assembly, testing and finally for research of the measurement platform. The second aspect of the study is to prepare, process and analyze the recorded ECG and EMG data by using MATLAB. Various filtering, denoising, processing and analysis algorithms will be developed and executed to extract the features of the ECG and EMG waveform structures. Finally, graphical representations will be made for the resulting outputs of the aforementioned techniques

Electromyography in the Study of Muscle Reactions to Vibration Treatment

Electromyography (EMG) is a common used technique to evaluate muscular activity. Analysis of EMG recordings is important for assessing muscle activation, its relationship to the force developed during specific tasks and for evaluating fatigue processes occurring in response to physical activity. Electromyography can be performed using different types of electrodes, depending on the specific analysis: surface (or skin) electrodes or inserted electrodes (wire and needle); the first it is used to monitor the overall activity of a muscle while the second is generally used to reveal the electrical activity of a nerve root. (De Luca, 1997, Basmajan and De Luca, 1985) Electrode types and configurations, as well as associated instrumentation, influence the quality of the EMG signal detected and displayed, recorded or processed (Merletti et al, 2001; Saitou et al, 2000; Rainoldi et al, 2004, Nishihara et al, 2008). Various studies have been dedicated to the matter and guidelines in EMG recording are available (Basmajan and De Luca, 1985, Hermens H.J. et al, 1999). Surface electromyography (SEMG) analysis is a largely used EMG recording method as it is non–invasive, safe, it does not cause pain and it is simple to perform. Root mean square (RMS) of the surface EMG signals is often used as a concise quantitative index of muscle activity; indeed, electromyography devices often provide EMG RMS output. SEMG is often used for the assessment of muscle activity occurring in response to physiological or to externally applied stimuli, i.e. vibratory stimulation. Vibration stimulus is a mechanical muscle excitation, applied generally to a tendon, a muscle or to the body as a whole, aimed to activate muscles by eliciting stretch reflexes. Local tendon vibrations induce activiy of the muscle spindle Ia fibers, mediated by monosynaptic and/or polysynaptic pathways; the reflex muscle contraction that arises in response to such vibratory stimulus has been named Tonic Vibration Reflex (TVR). (Roll et al, 1989; Bongiovanni and Hagbart, 1990; Romaiguére et al, 1991; Person and Kozhina, 1992; Martin and Park, 1997) As well as in other external stimulation, vibratory muscle activation can be examined by the analysis of electromyography recordings. Many studies report a significant increase of EMG RMS values in the lower body muscles during vibration training, these changes suggested an increase in neuromuscular activity (Cardinale and Bosco, 2003; Verschueren et al, 2004). Specific WBV frequencies seem to produce a higher EMG RMS signal than others (Cardinale and Lim 2003). However, as well as in every surface bio-potential recording, during local or whole body vibration treatment the EMG signal can be affected by artifacts. Motion artifacts may in fact arise from relative motion between electrodes and skin and also between skin layers. The only skin stretch may result in a variation of electrode potential (Turker, 1993, De Talhouet and Webster, 1996; Ödman and Öberg, 1982, Searle and Kirkup, 2000, Tam and Webster, 1977). In classical clinical EMG recordings (isokinetic, isotonic, gait, etc.), frequency content of motion artifact is considered below 10-20 Hz, then the general approach to motion artifact reduction is to apply a high-pass filter (e.g. with a cut-off frequency of 20 Hz). During vibratory stimulation the artifact frequency contents, typically limited at vibratory frequency and its har onics, extend within the EMG spectrum (Fratini et al, 2009) and standard high-pass filters are not suitable for filtering out this artifact. In the majority of the cases appropriate filtering is used to remove motion artifacts before any signal analysis, while in some other they are used to characterize the mechanical response of the tissue to a specific stimulus (mechanogram) and its correlation to the stimulus itself (Person and Kozhina, 1992; Fratini et al, 2009). With this chapter the authors aim to investigate the use and the efficacy of surface electromyography in the study of muscle response to vibration treatments. A review ofvibration characterization and analysis is reported, SEMG recordings of Rectus Femori, Vastus Medialis and Vastus Lateralis were collected and analyzed. Specific artifacts were revealed and the role of those artifact was investigated and assessed. Since the use of vibratory stimulus produces peculiar EMG response a specific model was adopted to describe the EMG synchronization effect and its influence on the resultant recorded muscle activity (Person and Kozhina, 1992)

Subband Independent Component Analysis for Coherence Enhancement

Objective: Cortico-muscular coherence (CMC) is becoming a common technique for detection and characterization of functional coupling between the motor cortex and muscle activity. It is typically evaluated between surface electromyogram (sEMG) and electroencephalogram (EEG) signals collected synchronously during controlled movement tasks. However, the presence of noise and activities unrelated to observed motor tasks in sEMG and EEG results in low CMC levels, which often makes functional coupling difficult to detect. Methods: In this paper, we introduce Coherent Subband Independent Component Analysis (CoSICA) to enhance synchronous cortico-muscular components in mixtures captured by sEMG and EEG. The methodology relies on filter bank processing to decompose sEMG and EEG signals into frequency bands. Then, it applies independent component analysis along with a component selection algorithm for re-synthesis of sEMG and EEG designed to maximize CMC levels. Results: We demonstrate the effectiveness of the proposed method in increasing CMC levels across different signal-to-noise ratios first using simulated data. Using neurophysiological data, we then illustrate that CoSICA processing achieves a pronounced enhancement of original CMC. Conclusion: Our findings suggest that the proposed technique provides an effective framework for improving coherence detection. Significance: The proposed methodologies will eventually contribute to understanding of movement control and has high potential for translation into clinical practice

Applications of EMG in Clinical and Sports Medicine

This second of two volumes on EMG (Electromyography) covers a wide range of clinical applications, as a complement to the methods discussed in volume 1. Topics range from gait and vibration analysis, through posture and falls prevention, to biofeedback in the treatment of neurologic swallowing impairment. The volume includes sections on back care, sports and performance medicine, gynecology/urology and orofacial function. Authors describe the procedures for their experimental studies with detailed and clear illustrations and references to the literature. The limitations of SEMG measures and methods for careful analysis are discussed. This broad compilation of articles discussing the use of EMG in both clinical and research applications demonstrates the utility of the method as a tool in a wide variety of disciplines and clinical fields

Subband Independent Component Analysis for Coherence Enhancement

Objective: Cortico-muscular coherence (CMC) is becoming a common technique for detection and characterization of functional coupling between the motor cortex and muscle activity. It is typically evaluated between surface electromyo- gram (sEMG) and electroencephalogram (EEG) signals collected synchronously during controlled movement tasks. However, the presence of noise and activities unrelated to observed motor tasks in sEMG and EEG results in low CMC levels, which often makes functional coupling difficult to detect. Methods: In this paper, we introduce Coherent Subband Independent Component Analysis (CoSICA) to enhance synchronous cortico-muscular components in mixtures captured by sEMG and EEG. The methodology relies on filter bank processing to decompose sEMG and EEG signals into frequency bands. Then, it applies independent component analysis along with a component selection algorithm for re- synthesis of sEMG and EEG designed to maximize CMC levels. Results: We demonstrate the effectiveness of the proposed method in increasing CMC levels across different signal-to-noise ratios first using simulated data. Using neurophysiological data, we then illustrate that CoSICA processing achieves a pronounced enhancement of original CMC. Conclusion: Our findings suggest that the proposed technique provides an effective framework for improving coherence detection. Significance: The proposed methodologies will eventually contribute to understanding of movement control and has high potential for translation into clinical practice

Automatic Pain Assessment by Learning from Multiple Biopotentials

Kivun täsmällinen arviointi on tärkeää kivunhallinnassa, erityisesti sairaan- hoitoa vaativille ipupotilaille. Kipu on subjektiivista, sillä se ei ole pelkästään aistituntemus, vaan siihen saattaa liittyä myös tunnekokemuksia. Tällöin itsearviointiin perustuvat kipuasteikot ovat tärkein työkalu, niin auan kun potilas pystyy kokemuksensa arvioimaan. Arviointi on kuitenkin haasteellista potilailla, jotka eivät itse pysty kertomaan kivustaan. Kliinisessä hoito- työssä kipua pyritään objektiivisesti arvioimaan esimerkiksi havainnoimalla fysiologisia muuttujia kuten sykettä ja käyttäytymistä esimerkiksi potilaan kasvonilmeiden perusteella. Tutkimuksen päätavoitteena on automatisoida arviointiprosessi hyödyntämällä koneoppimismenetelmiä yhdessä biosignaalien prosessointnin kanssa. Tavoitteen saavuttamiseksi mitattiin autonomista keskushermoston toimintaa kuvastavia biopotentiaaleja: sydänsähkökäyrää, galvaanista ihoreaktiota ja kasvolihasliikkeitä mittaavaa lihassähkökäyrää. Mittaukset tehtiin terveillä vapaaehtoisilla, joille aiheutettiin kokeellista kipuärsykettä. Järestelmän kehittämiseen tarvittavaa tietokantaa varten rakennettiin biopotentiaaleja keräävä Internet of Things -pohjainen tallennusjärjestelmä. Koostetun tietokannan avulla kehitettiin biosignaaleille prosessointimenetelmä jatku- vaan kivun arviointiin. Signaaleista eroteltiin piirteitä sekuntitasoon mukautetuilla aikaikkunoilla. Piirteet visualisoitiin ja tarkasteltiin eri luokittelijoilla kivun ja kiputason tunnistamiseksi. Parhailla luokittelumenetelmillä saavutettiin kivuntunnistukseen 90% herkkyyskyky (sensitivity) ja 84% erottelukyky (specificity) ja kivun voimakkuuden arviointiin 62,5% tarkkuus (accuracy). Tulokset vahvistavat kyseisen käsittelytavan käyttökelpoisuuden erityis- esti tunnistettaessa kipua yksittäisessä arviointi-ikkunassa. Tutkimus vahvistaa biopotentiaalien avulla kehitettävän automatisoidun kivun arvioinnin toteutettavuuden kokeellisella kivulla, rohkaisten etenemään todellisen kivun tutkimiseen samoilla menetelmillä. Menetelmää kehitettäessä suoritettiin lisäksi vertailua ja yhteenvetoa automaattiseen kivuntunnistukseen kehitettyjen eri tutkimusten välisistä samankaltaisuuksista ja eroista. Tarkastelussa löytyi signaalien eroavaisuuksien lisäksi tutkimusmuotojen aiheuttamaa eroa arviointitavoitteisiin, mikä hankaloitti tutkimusten vertailua. Lisäksi pohdit- tiin mitkä perinteisten prosessointitapojen osiot rajoittavat tai edistävät ennustekykyä ja miten, sekä tuoko optimointi läpimurtoa järjestelmän näkökulmasta.Accurate pain assessment plays an important role in proper pain management, especially among hospitalized people experience acute pain. Pain is subjective in nature which is not only a sensory feeling but could also combine affective factors. Therefore self-report pain scales are the main assessment tools as long as patients are able to self-report. However, it remains a challenge to assess the pain from the patients who cannot self-report. In clinical practice, physiological parameters like heart rate and pain behaviors including facial expressions are observed as empirical references to infer pain objectively. The main aim of this study is to automate such process by leveraging machine learning methods and biosignal processing. To achieve this goal, biopotentials reflecting autonomic nervous system activities including electrocardiogram and galvanic skin response, and facial expressions measured with facial electromyograms were recorded from healthy volunteers undergoing experimental pain stimulus. IoT-enabled biopotential acquisition systems were developed to build the database aiming at providing compact and wearable solutions. Using the database, a biosignal processing flow was developed for continuous pain estimation. Signal features were extracted with customized time window lengths and updated every second. The extracted features were visualized and fed into multiple classifiers trained to estimate the presence of pain and pain intensity separately. Among the tested classifiers, the best pain presence estimating sensitivity achieved was 90% (specificity 84%) and the best pain intensity estimation accuracy achieved was 62.5%. The results show the validity of the proposed processing flow, especially in pain presence estimation at window level. This study adds one more piece of evidence on the feasibility of developing an automatic pain assessment tool from biopotentials, thus providing the confidence to move forward to real pain cases. In addition to the method development, the similarities and differences between automatic pain assessment studies were compared and summarized. It was found that in addition to the diversity of signals, the estimation goals also differed as a result of different study designs which made cross dataset comparison challenging. We also tried to discuss which parts in the classical processing flow would limit or boost the prediction performance and whether optimization can bring a breakthrough from the system’s perspective

Techniques of EMG signal analysis: detection, processing, classification and applications

Electromyography (EMG) signals can be used for clinical/biomedical applications, Evolvable Hardware Chip (EHW) development, and modern human computer interaction. EMG signals acquired from muscles require advanced methods for detection, decomposition, processing, and classification. The purpose of this paper is to illustrate the various methodologies and algorithms for EMG signal analysis to provide efficient and effective ways of understanding the signal and its nature. We further point up some of the hardware implementations using EMG focusing on applications related to prosthetic hand control, grasp recognition, and human computer interaction. A comparison study is also given to show performance of various EMG signal analysis methods. This paper provides researchers a good understanding of EMG signal and its analysis procedures. This knowledge will help them develop more powerful, flexible, and efficient applications