With the rapid development wireless technologies, mobile phones are gaining acceptance to become an effective tool for cardiovascular monitoring. However, existing technologies have limitations in terms of efficient transmission of compressed ECG over text messaging communications like SMS and MMS. In this paper, we first propose an ECG compression algorithm which allows lossless transmission of compressed ECG over bandwidth constrained wireless link. Then, we propose several algorithms for cardiovascular abnormality detection directly from the compressed ECG maintaining end to end security, patient privacy while offering the benefits of faster diagnosis. Next, we show that our mobile phone based cardiovascular monitoring solution is capable of harnessing up to 6.72 times faster diagnosis compared to existing technologies. As the decompression time on a doctor's mobile phone could be significant, our method will be highly advantageous in patient wellness monitoring system where a doctor has to read and diagnose from compressed ECGs of several patients assigned to him. Finally, we successfully implemented the prototype system by establishing mobile phone based cardiovascular patient monitoring.

Novel methods of faster cardiovascular diagnosis in wireless telecardiology

Realtime telemonitoring of critical, acute and chronic patients has become increasingly popular with the emergence of portable acquisition devices and IP enabled mobile phones. During telemonitoring, enormous physiological signals are transmitted through the public communication network in realtime. However, these physiological signals can be intercepted with minimal effort, since existing telemonitoring practise ignores the privacy and security requirements. In this paper, to achieve end-to-end security, we first proposed an encoding method capable of securing Electrocardiogram (ECG) data transmission from an acquisition device to a mobile phone, and then from a mobile phone to a centralised medical server by concealing cardiovascular details as well as features in ECG data required to identify an individual. The encoding method not only conceals cardiovascular condition, but also reduces the enormous file size of the ECG with a compression ratio of up to 3.84, thus making it suitable in energy constrained small acquisition devices. As ECG data transfer faces even greater security vulnerabilities while traversing through the public Internet, we further designed and implemented 3 phase encoding-compression-encryption mechanism on mobile phones using the proposed encoding method and existing compression and encryption tools. This new mechanism elevates the security strength of the system even further. Apart from higher security, we also achieved higher compression ratio of up to 20.06, which will enable faster transmission and make the system suitable for realtime telemonitoring.

Enforcing secured ECG transmission for realtime telemonitoring: A joint encoding, compression, encryption mechanism

Digital technology transforms analog signals into digital signals. Digitalization translates data into a numerical sequence of 0 seconds and 1 second or off and on (binary system). Thus encoded, differing signals (static or moving images, sounds, written texts) become homogeneous and can be handled simultaneously in a rapid and flexible manner while maintaining quality and stability. In digital format, data are easy to handle and can be compressed without undergoing any alteration of their contents, creating a vast wealth of information contained in little space, as in digital libraries.

Digital technology for cardiac pacing

Implantable devices have limited memory, computational and battery power resources, while collecting, processing and transmitting out information from potentially many sensors. These limitations require that information within the devices be efficiently compressed. Such data compression presents a challenging task, as it must provide high fidelity of the waveform reproduction and high compression ratios on limited size data frames. Also, it must efficiently run on ultra low power hardware, and allow flexible configuration, based on the type of data to be compressed. The new compression algorithm was implemented as a bit accurate Matlab simulation, consisting of the following major steps: 1. Integer wavelet transform. 2. Quantization coupled with filtration. Two selectable quantization schemes could be utilized based on the signal properties: linear and dead-zone. Data filtration is performed on bit boundaries, which simplifies hardware implementation. The filtration thresholds are made different in different wavelet sub-bands, controlled by a single parameter. 3. Original adaptive data encoding. Our approach only requires basic logical operations such as bit counters and shifts, and is highly optimized for implementation in implantable device hardware. For high reliability each compressed data frame contains all information needed for decompression. The algorithm was applied to data from the PhysioNet ECG compression test database. 40 ECG frames of 1024 samples from 4 patients were sampled at 250 Hz, 12 bit resolution, and compressed with distortions below 8%. Compression ratios were 9.3plusmn2.5, consistently exceeding 85% of the theoretical limit based on bit entropy for each individual data frame. It is concluded The compression algorithm is efficient on data collected by implantable devices, and could be used in various applications in both microprocessor and ASIC implementations, helping to reduce memory requirements and the battery energy spent on the information transmission to and from the implantable device.

http://cinc.mit.edu/archives/2008/pdf/0417.pdf

Data compression for implantable medical devices

Proper functioning of a pacemaker depends exclusively on the detection of intrinsic cardiac signals. Conventional devices merely establish the presence of an endocavitary potential, but do not evaluate it qualitatively. The recent incorporation of digital signal processing of sensed events in pacemakers permits diagnostic characterization heretofore impossible. Methods: Digital processing was used in 268 cardiac cycles recorded from 5 patients during elective dual chamber pacemaker replacement, The signals were classified in real time by an external pacemaker equipped with hardware and software capabilities for this purpose. Results: The parameters used for morphological analysis were the minimum value of the slope signal and the minimum value of the filtered signal. The discrimination of a true atrial signal from a far-field R wave was correct in every patient (p < 0.0001). Conclusion: Digital devices may monitor and classify every event continually, and dramatically increase device reliability and the amount of information that can be processed and stored.

Digital Technology in Cardiac Pacing: Methods for Morphology Analysis of Sensed Endocavitary Signals

Aim This paper reports studies of mathematical algorithms for intra-cardiac atrial bipolar electrogram compression suitable with implementation on implantable devices. Patients and Methods Bipolar intra-cardiac electrograms (IEGMs) of high right atrium were obtained from 20 patients who underwent electrophysiological studies for arrhythmias. Four thousand seven hundred and eighty-two seconds of IEGM were collected and divided into three rhythm groups: sinus rhythm (SR), atrial fibrillation (AF) and atrial flutter (AFL). Since mathematical algorithms suitable for use with implantable devices demand low computational cost, we employed piecemeal linear approximation methods (ZOP — Zero Order Prediction and SAPA — Scan Along Polygonal Approximation), and beat detection method (Peak) both or which need small numbers of operations to perform electrogram compression. Compression ratio (CR) and percent root mean square difference (PRD) were used to compare the three methods, with statistical analyses performed using paired t-test. Results and Conclusion The best performance was obtained using the Peak method which reaches an average CR of 10·6 in the case of SR group, 2·8 for AF, and 3·6 for AFL groups, respectively, while PRD lies below 2% for SR and AFL groups and 6% for the AF group. Results show that, for bipolar electrograms, the Peak method reaches statistically significant better performance (P<0·001) in all cases except for Peak vs SAPA applied to AF (P=0·2). The number of operations necessary to compress the data indicate that time consumption can be reduced to be suitable for real time compression in implantable devices. The Peak method, which was assumed to receive the instant of occurrence of each recognized beat, from the hardware of the device, requires fewer operations than ZOP and SAPA. Increasing the length of electrograms recorded in pacemakers will enhance the amount of information provided by the implantable device, allowing more detailed characterization of the intra-cardiac activity and leading to new perspectives in arrhythmia diagnosis and therapy. (Europace 2002; 4: 295–302)

/pdf/computationally-inexpensive-methods-for-intra-cardiac-atrial-2pfl07hnq3.pdf

Computationally inexpensive methods for intra-cardiac atrial bipolar electrogram compression.

This paper studies methods for intracardiac atrial electrograms compression suitable with implementation on implantable devices. Algorithms are based an piecewise linear approximation, beat detection, and their combination. Bipolar intracardiac electrograms were obtained during electrophysiological studies and divided into 3 rhythm groups. The authors analyzed 2196 seconds of sinus rhythm (SR), 786 sec. of atrial fibrillation (AF) and 1793 sec. of atrial flutter (AFL). Performance over the whole data base reach average compressed data rate (CDR) ranging between 500 and 850 (bits/second), and the percent of root mean square difference (PRD) varies from 2 to 12% depending on the choice of methods and accepted errors. Preliminary results show that time consumption can be reduced to enable real time compression with implanted devices.

http://ieeexplore.ieee.org/iel5/7213/19426/00898584.pdf

Low complexity methods for intracardiac atrial electrogram compression

BACKGROUND Automatic atrial tachyarrhythmia recognition is crucial in order to allow a correct switching-mode function of dual-chamber pacemakers and to avoid inappropriate shocks of ventricular implantable cardioverter-defibrillators. In this paper we considered three algorithms suitable for implantable devices. The first was based on the atrial cycle length; the others analyze different morphologic characteristics of atrial signals. METHODS Intracardiac bipolar electrogram recordings were obtained from the high right atrium during electrophysiological study. Twenty patients were considered, some of them presenting with different types of cardiac rhythm at different intervals of the study. Cardiac rhythms were divided into three groups: sinus rhythm consisting of 2,196 s obtained from 12 subjects, atrial fibrillation consisting of 771 s obtained from 7 subjects, and atrial flutter consisting of 1,793 s obtained from 7 subjects. The automatic detection was performed on each electrogram segment lasting 1 or 4 s. Atrial segments were separated into two subgroups: the first for the training of the algorithm and the second for testing and validation of results. We considered two types of statistical analysis: comparison between pairs of rhythm (paired classification), and classification among the three different groups (direct classification). RESULTS The combination of the cycle length algorithm with a morphological method achieved the best performance for both statistical analyses. Paired classification resulted in the following: atrial fibrillation vs sinus rhythm was detected with no error; atrial flutter vs sinus rhythm with a total accuracy of 99.3% (sensitivity 99.4%, specificity 99.2%); atrial fibrillation vs atrial flutter with a total accuracy of 99.1% (sensitivity 98.5%, specificity 99.4%). The total accuracy achieved for the direct classification was 98.6% (average sensitivity 98.5%, specificity 98.8%). CONCLUSIONS Our results support the association of algorithms for future enhancement of atrial tachyarrhythmia detection in dual-chamber devices, thanks to the limited computational effort.

Automatic atrial tachyarrhythmia detection from intracardiac electrograms.

Studies methods for intracardiac electrogram compression that are suitable for implementation in implantable devices. The algorithms are based on piecewise linear approximation (PLA) methods and beat detection (the peak method). Intracardiac electrograms were obtained, from the right atrium and ventricle, during electrophysiological studies. The total atrial set consists of 5060 s of bipolar recordings and 680 s of unipolar electrograms; the ventricular data set contains 1210 s of bipolar and 480 s of unipolar signals. The peak method clearly performs better than the others to compress bipolar signals, while PLA methods are needed in order to have reliably compressed unipolar data. Performances over the whole bipolar database (including atrial and ventricular sets) reached an average compression ratio (CR) of 7.6, while first-order piecewise linear approximation on unipolar electrograms reached CR=6.6. These preliminary results show that time consumption can be reduced suitably with real-time compression for implantable devices. The ability to compress and store intracardiac electrograms in implantable devices allows detailed verification of appropriate intervention of the device and it might open up new perspectives in the study of the mechanisms involved in the onset of malignant tachyarrhythmias.

Methods for intracardiac electrogram compression suitable with implantable devices

This paper analyzes the Middle Amplitude Range (MAR) of Intracardiac Atrial Electrograms. Electrograms are obtained using bipolar catheters from the High Right Atrium during Electrophysiological (EP) studies. Three groups of stable rhythm are considered: atrial fibrillation (AF), atrial flutter (AFL) and sinus rhythm (SR). EP studies are obtained from 10 subjects many of whom presented different rhythms in different intervals of the study. The major purpose of the work is to propose a method, compatible with implementation on implantable devices, able to discriminate among SR, AF and AFL in a defined decision time interval. This aim is achieved by combining the analyses based on cycle length and MAR. The results are presented in terms of sensitivity, specificity and total accuracy for various choices of the decision time interval. The best performances are obtained for a 4 second interval and give a total accuracy of 98.9%, with specificity of 99.2% and average sensitivity of 98.8%.

https://ieeexplore.ieee.org/iel5/6622/17673/00826013.pdf

M. Reggiani

Papers

Computationally inexpensive methods for intra-cardiac atrial bipolar electrogram compression.

Low complexity methods for intracardiac atrial electrogram compression

Automatic atrial tachyarrhythmia detection from intracardiac electrograms.

Methods for intracardiac electrogram compression suitable with implantable devices

Middle amplitude range analysis for intracardiac atrial electrograms classification