A computationally efficient QRS detection algorithm for wearable ECG sensors

10.1109/IEMBS.2011.6091365Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS5641-564

A washable silver-printed textile electrode for ECG monitoring

Electrocardiography (ECG) is one of the most widely used diagnostic methods to examine the development of cardiovascular diseases (CVD). It is important to have a long-term continuous ECG recording to properly monitor the heart activity, which can be measured by placing two or more electrodes on the skin. Ag/AgCl gelled electrodes are often used for the ECG measurement, but they are not suitable for long-term monitoring due to the dehydration of the gel over time and skin irritation. Textile-based electrodes could have an important role in replacing the gelled electrodes and avoid their associated problems. This paper focuses on the development of a textile-based electrode and studying its ECG detecting performance. We developed silver printed textile electrodes via a flat-screen printing of silver ink on knitted polyester fabric. The surface resistance of silver-coated PET fabric was 1.78 Ω/sq and 3.77 Ω/sq before and after washing, respectively. Stretching of the conductive fabric from 5% to 40% caused a 6% to 18.28% increase in surface resistance. The silver-printed PET fabric stayed reasonably conductive after washing and stretching which makes it suitable for wearable applications. Moreover, the ECG measurement at static condition showed that the signal quality collected before and after washing were comparable with the Ag/AgCl standard electrodes. The P, QRS, T waveforms, and heartbeat before washing in respective order were 0.09 mV, 1.20 mV, 0.30 mV for the silver printed fabric electrode and 72 bpm, and 0.10 mV, 1.21 mV, 0.30 mV, and 76 bpm for Ag/AgCl standard electrode

Verification and comparison of MIT-BIH arrhythmia database based on number of beats

The ECG signal processing methods are tested and evaluated based on many databases. The most ECG database used for many researchers is the MIT-BIH arrhythmia database. The QRS-detection algorithms are essential for ECG analyses to detect the beats for the ECG signal. There is no standard number of beats for this database that are used from numerous researches. Different beat numbers are calculated for the researchers depending on the difference in understanding the annotation file. In this paper, the beat numbers for existing methods are studied and compared to find the correct beat number that should be used. We propose a simple function to standardize the beats number for any ECG PhysioNet database to improve the waveform database toolbox (WFDB) for the MATLAB program. This function is based on the annotation's description from the databases and can be added to the Toolbox. The function is removed the non-beats annotation without any errors. The results show a high percentage of 71% from the reviewed methods used an incorrect number of beats for this database

Development of washable silver printed textile electrodes for long-term ECG monitoring

Long-term electrocardiography (ECG) monitoring is very essential for the early detection and treatment of cardiovascular disorders. However, commercially used silver/silver chloride (Ag/AgCl) electrodes have drawbacks, and these become more obvious during long-term signal monitoring, making them inconvenient for this use. In this study, we developed silver printed textile electrodes from knitted cotton and polyester fabric for ECG monitoring. The surface resistance of printed electrodes was 1.64 Ω/sq for cotton and 1.78 Ω/sq for polyester electrodes. The ECG detection performance of the electrodes was studied by placing three electrodes around the wrist where the electrodes were embedded on an elastic strap with Velcro. The ECG signals collected using textile electrodes had a comparable waveform to those acquired using standard Ag/AgCl electrodes with a signal to noise ratio (SNR) of 33.10, 30.17, and 33.52 dB for signals collected from cotton, polyester, and Ag/AgCl electrodes, respectively. The signal quality increased as the tightness of the elastic strap increased. Signals acquired at 15 mmHg pressure level with the textile electrodes provided a similar quality to those acquired using standard electrodes. Interestingly, the textile electrodes gave acceptable signal quality even after ten washing cycles

Wearable smart textiles for long-term electrocardiography monitoring : a review

The continuous and long-term measurement and monitoring of physiological signals such as electrocardiography (ECG) are very important for the early detection and treatment of heart disorders at an early stage prior to a serious condition occurring. The increasing demand for the continuous monitoring of the ECG signal needs the rapid development of wearable electronic technology. During wearable ECG monitoring, the electrodes are the main components that affect the signal quality and comfort of the user. This review assesses the application of textile electrodes for ECG monitoring from the fundamentals to the latest developments and prospects for their future fate. The fabrication techniques of textile electrodes and their performance in terms of skin–electrode contact impedance, motion artifacts and signal quality are also reviewed and discussed. Textile electrodes can be fabricated by integrating thin metal fiber during the manufacturing stage of textile products or by coating textiles with conductive materials like metal inks, carbon mate-rials, or conductive polymers. The review also discusses how textile electrodes for ECG function via direct skin contact or via a non-contact capacitive coupling. Finally, the current intensive and promising research towards finding textile-based ECG electrodes with better comfort and signal quality in the fields of textile, material, medical and electrical engineering are presented as a perspective

Computationally Efficient QRS Detection Analysis In Electrocardiogram Based On Dual-Slope Method

A dramatic growth of interest for wearable technology has been fostered by recent technological advances in sensors, low-power integrated circuits and wireless communications. This interest originates from the need of monitoring a patient over extensive period of time. For cardiac patients, wearable heart monitoring sensors have already become a life-saving intervention ensuring continuous monitoring during daily life. Therefore, it is essential for an accurate monitoring and diagnosis of heart patients. Patients can be equipped with wireless, miniature and lightweight sensors. The sensors temporarily store physiological data and then periodically upload the data to a database server. These recorded data sets are then analyzed to predict any possibility of worsening patient\u27s situation or explored to assess the effect of clinical intervention. To obtain accurate response with less computational complexity as well as long battery life time, there is a demand of developing fast and accurate algorithm and prototypes for wearable heart monitoring sensors. A computationally efficient QRS detection algorithm is indispensable for low power operation on electrocardiogram (ECG) signal. In need of detecting QRS complex, most of the early works were proposed based on derivatives of ECG signal. They can be easily implemented with high computational speed. But owing to the inherent variability in ECG, these methods are highly affected by large derivatives of baseline noises. Algorithms based on neural network (NN) showed relatively robust performance against noise but requires exhaustive training and estimation of model parameter. On the other hand, wavelet based methods have the choice problem of mother wavelet. Hence, none of these methods is suitable for giving a long battery performance in wearable devices with high accuracy. Recently, Wang et al. proposed a novel dual slope QRS detection algorithm which has less computational complexity as well as high accuracy. Considering that the width of the QRS complex is relatively fixed, this algorithm is based on the fact that the largest change of slope usually happens at the peak of QRS complex. The hardware requirement is also low. However, the method has a set of time consuming slope calculations on both sides of each sample. To avoid such time consuming slope calculation, only one sample on each side can be highlighted. In addition, the multiplication of the left and right hand side slope should give us a very high value in QRS complex. The goal of this thesis is to develop a new computationally efficient method to detect QRS complexes and compare with the other renowned QRS detection algorithms. MIT-BIH arrhythmia database based on patients of different heart diseases and database containing ECG from healthy subjects are used. To analyze the performance, false negative (FN) and false positive (FP) are evaluated. A false negative (FN) occurs when algorithm fails to detect an actual QRS complex quoted in the corresponding annotation file of the database record and a false positive (FP) means a false beat detection. Error rate (ER) , Sensitivity (Se) and Specificity (Sp) are calculated using FP and FN