Integrated mobile electrocardiography

Abstract -Modern mobile electrocardiographic equipment converges various technologies which contribute to the speed of reaction in critical situations and contributes to the quality of life of the patient. Such convergence is valuable, but solutions are still immature and there are certain problems which should be solved before full implementation. This article describes one solution of integrated mobile electrocardiograph which comprises mobile ECG, global positioning system, UMTS/GPRS transfer of data and Web services for connection of distributed components. The system records electrocardiogram, detects rhythm anomalies and immediately alerts doctor sending critical ECG segment and location of the patient. The doctor can contact the patient and send nearest ambulance by the optimal route. The fact that system works in real time and locates the patient might be crucial in certain situations. It has excellent potential, but requires technical and organizational infrastructure which will support its functioning

Applications of GSM Module in Wireless ECG Signal Monitoring System

Abstract: In this paper, a wireless ECG monitoring system which can not only sense the ECG signal but also analyze the signal and in abnormal situation transmits the ECG signal for instant action. An ECG is used to measure the heart's electrical conduction system. It picks up electrical impulses generated by the polarization and depolarization of cardiac tissue and translates into a waveform. The waveform is then used to measure the rate and regularity of heartbeats, as well as the size and position of the chambers, the presence of any damage to the heart, and the effects of drugs or devices used to regulate the heart, such as a pacemaker. The fully system will be basically for a high cardiac diseased person and if they are moving or they are living alone this system will be very helpful to them

Artificial intelligence method for time series data mining - implementation on the human ECG signal

Tehnološki razvoj u modernom društvu kao jednu od posljedica ima i povećan broj generiranih podataka čija brojnost predstavlja značajan tehnički i znanstveni izazov u smislu pohrane i obrade.Za podatke koji u sebi ne sadrže vremensku komponentu, metode i tehnike za pohranu i analizu su veoma razvijene, ali za podatke koji su producirani slijedno ovo je još uvijek izazov. Podaci u vremenskim serijama nisu podobni za analizu klasičnim statističkim metodama jer je svaki podatak mjerenja direktno ovisan o prethodnom podatku mjerenom na istom izvoru. Ovime je prekršeno temeljno načelo klasičnih statističkih metoda o nezavisnosti opservacija u uzorku. Jedan od složenijih problema u analizi vremenskih serija je analiza elektrokardiograma (EKG-a). Ovaj rad predlaženovu metodu za analizu vremenskih serija te predstavlja istraživanje u kojem je ista metoda primijenjena u analizi ljudskog EKG signala. EKG kao postupak relativno niskih troškova koji je k tome ineinvazivan jest jedna od osnovnih dijagnostičkih metoda. Kako dugotrajno pregledavanje mnogo-brojnih EKG valova može biti naporno i neprakticno za ljudskog eksperta, računalna analiza EKG signala je značajan znanstveni i tehnički izazov s mnogim potencijalnim primjenama. Problem analize EKG signala obuhvaća nekoliko podrucja istraživanja poput uklanjanja šumova i smetnjikoje nastaju tijekom snimanja, detekcije otkucaja srca, analize ritma te raspoznavanja oblika EKG valova. Ovo istraživanje fokusirano je na detekciju otkucaja srca i raspoznavanje oblika valova.Inspiracija za razvoj metode dolazi iz spoznaja racunalne neuroznanosti, a metoda je u okviruovog istraživanja implementirana u programskom jeziku C++. Provedeni su eksperimenti u detekciji QRS kompleksa bez filtriranja signala, te detekciji QRS kompleksa i prepoznavanja oblikavalova nakon filtriranja signala. U tu svrhu su implementirani i digitalni filtri. U istraživanjusu dobiveni rezultati koji nadmašuju trenutno stanje tehnike te su dobivene spoznaje za daljnjirazvoj i primjenu metode i u podrucju racunalnog vida. Postignuta je tocnost detekcije otkucaja srca bez primjene filtara u prosjeku iznad 95% izracunato prema metodi unakrsne validacije nadsvakim zapisom, te iznad 99% nakon filtriranja signala prema više realisticnoj metodi testiranja baziranoj na subjektu te iznad 96% u raspoznavanju oblika EKG valova testirano prema prepo-rukama AAMI standarda. Takvo testiranje realno simulira potencijalnu klinicku primjenu. U smislu racunalnog vida, provedeni su eksperimenti u raspoznavanju rukom napisanih brojeva i drugih dvodimenzionalnih oblika. Rezultati u tim eksperimentima su približni trenutnom stanjutehnike i kreću se oko 90% tocnosti u raspoznavanju rukom napisanih brojeva iz MNIST skupa podataka.In this research, a new method (algorithm) of artificial intelligence for pattern recognition is proposed. The method is based on principles of human perception and it is a part of computer engineering domain, the field of artificial intelligence. Method is the result of perennial scientific research and development. The main implementation of the algorithm within the project is on the example of the human ECG signal analysis, which is one of the most demanding problems within the field of time series analysis. Research scope included software implementation and testing on the officially recognized databases of the human ECG signal (MIT-BIH Arrhythmia Database) by using the scientifically recognized metrics (specificity, sensitivity, positive predictivity etc.). The essence of the method is its algorithm, which, in the authors opinion, reminds of human perception principles. In the scientific literature no similar approach is yet known. The approach is based ona study of a specific field in Computational Neuroscience and also, on the conclusions about how brain neurons perceive stimuli coming from sensors (human senses). Beyond the analysis of ECG signals, the above method has many other applications, such as applications in finance, industry,energy, computer vision (recognition of 2D and 3D shapes or photographs after pre-processing) etc. Results achieved in the research are competitive with the current state of the art methods.Without signal filtering, QRS detection is accurate in more than 95% cases. After signal filtering, accuracy is above 99% tested with the subject-based methodology, which is the most realistic one. Heartbeat classification is accurate above 96% tested by the AAMI standard methodology.Handwritten character recognition is accurate around 90% (MNIST dataset). Methods are implemented in C++ programming language

Wearable ECG Recognition and Monitor *

ECG(Electrocardiogram) recognition and monitor are inevitable to trace and determine heart diseases. As self-health being focused on and social medical grade being progressed, ECG monitors with features such as portable/wearable, wireless, use-friendly, low-cost and convenient at home, are more and more necessary. Unfortunately, such kind of equipments couldn’t be got currently. Thus, wearable ECG recognition and monitor instrument is developed. Palm, mobile phone and PC could be acting as display and relay terminals, where ECG signals would be transmitted to service center(e.g. hospital) through GSM/GPRS/CDMA and Internet. After introducing system architecture, the paper describes software design, direct ECG recognition method with morphology parameters based on specialists ’ experiences. The first generation product includes wearable monitor and Palm is ready now, which has huge market. 1