Displaying computerized electrocardiogram recordings on smartphones

Poster Session 1 / Journal of Electrocardiology 42 (2009) 607–613 Conclusion: The automated Selvester scoring system we implemented has high accuracy and comparable to expert manual scoring. We observed that the largest score differences between manual and automated resulted from the classification of rS and Q patterns. The computer algorithm is designed not to detect small initial deflections (∼20 μV), which human readers code as R waves when using a ×4 high-gain display. doi:10.1016/j.jelectrocard.2009.08.017

Detection of periodic variations including T-wave Alternans Eric D. Helfenbein, A. Dean Forbes, James M. Lindauer, Saeed Babaeizadeh, Sophia H. Zhou, (Advanced Algorithm Research Center, Philips Healthcare, Thousand Oaks, CA, USA) Introduction: Metabolically compromised tissue may respond to stimuli in a patterned matter. T-wave alternans (TWA) is one form of a patterned behavior in ECG that may be predictive of SCD. We introduce Microvolt Electrocardiogram (ECG) Amplitude Periodicity (MEAP) to detect TWA by analyzing body-surface ECG. Methods: Microvolt ECG Amplitude Periodicity is a time-domain approach to detect TWA buried in additive noise and modulating interferences. It filters the ECG to remove the noise and baseline wander and then estimates and suppresses the components due to respiration and exercise motion. To determine the presence or absence of TWA, a set of basis functions is generated, which represent the repeating patterns that are to be detected (including alternans). A model then is fit into the observed data by finding a linear combination of the basis functions. The coefficients corresponding to each period are used to compute the pattern power and amplitude for that period. Then, MEAP uses a generalized likelihood ratio test to calculate a measure of signal-to-noise ratio defined as the power in the observations “explained” and power “not explained” by the model. Generalized likelihood ratios computed for the TWA period represent the likelihood that TWA of that period is present. Results: Electrocardiograms were recorded from 50 patients with implantable cardiac defibrillator (ICD) while pedaling a stationary bike following a specific protocol. Each recording was 16 channels, 2000 sps, 140 nV LSB in ±5.5-mV offset-adjustable window, and frequency response of 0 to 500 Hz. Low-current respiratory impedance, electromagnetic interference (EMI), and bicycle pedal revolutions were also acquired. The study subjects were stable patients and were mostly on βblockers and amiodarone (which probably suppressed alternans in most patients). Microvolt ECG Amplitude Periodicity detected positive TWA with the peak amplitude of 4 μV on 1 patient. It was also tested on the PhysioNet CinC Challenge 2008 database (n = 100) and achieved sensitivity of 77%, specificity of 99%, PPV of 96%, and NPV of 91%, with only 1 false positive. Conclusion: Microvolt ECG Amplitude Periodicity is a highly specific and noise-tolerant algorithm to detect patterned beat-to-beat variability including TWA. More research is necessary to characterize its sensitivity and to determine if any periodic variation in QRS-T complex other than TWA may be used as a predictor of SCD. doi:10.1016/j.jelectrocard.2009.08.018

Displaying computerized electrocardiogram recordings on smartphones T. Hilbel, a S. Klug, a R.L. Lux, b H.A. Katus, c ( aUniversity of Applied Sciences, Gelsenkirchen, Germany; bUniversity of Utah, Salt Lake City, UT, USA; cUniversity of Heidelberg, Heidelberg, Germany) Introduction: Smartphones combine functions of mobile phones, personal digital assistants, and pocket PCs, and their use for business, communication, and entertainment is growing exponentially. Interestingly, biomedical software applications can be developed easily for smartphone devices that support Windows Mobile or Java. Because smartphones are popular in all age groups, one could posit that they might be useful as a portable health monitor for physiologic parameters and as an electrocardiogram (ECG) imaging device. Advantages of these portable minicomputers are that they

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are light and compact and support wireless communication and even eye-toeye video communication. In addition, sensors for vital parameters can easily be connected to such devices, and physiologically monitored parameters could be sent wireless from anywhere in the world to telemedicine health care centers or personalized fitness portals. Methods: To test and judge the suitability of smartphones as an ECG imaging device, an ECG viewer for a smartphone was developed. The program was written for Windows Mobile 6.1 using Microsoft C-Sharp. The graphical user interface was designed to display continuous, digitally recorded 12-lead ECG data and also discrete values of blood pressure and oxygen saturation. All data for display and analysis were stored and retrieved from the smartphone's memory card. The smartphone had a 300-MHz CPU, 64-MB RAM, and a 2.4inch touch screen with a resolution of 240 ⁎ 320 pixels. Results: With minimal programming effort, a program was developed that displayed ECG tracings and discrete physiologic values (heart rate, blood pressure, or pulse oximetry values) on the smartphone screen. The small smartphone display size limited viewing and monitoring of continuous ECG tracings. Especially for older people, a 2.40-inch touch screen with a resolution of 240 ⁎ 320 pixels may be difficult to see and operate. A 2.4inch' smartphone display is suitable for 2 ECG channels and a recording time of 1 to 3 seconds. It is not feasible to display a reasonable 12-lead ECG. The scrolling speed is also limited due to limited computation power of its CPU and its graphic chips. Nevertheless, due to their multiple wireless communication capabilities (GSM, Bluetooth, WiFi), the smartphone software is useful for the mobile transmission of physiologic data. Conclusions: Visualization of multichannel ECG recordings requires a high-resolution display and considerable memory, both of which are limited on small, low-power consumption mobile phones, thus limiting their usefulness as 12-lead ECG viewers. They would be suitable for display of 2channel ECGs and discrete vital parameters and very helpful for the wireless transmission of a variety of physiologic data. We have demonstrated that biomedical programs for smartphones can easily be developed using standardized Windows Mobile programming tools. doi:10.1016/j.jelectrocard.2009.08.019

Considerations for clinical studies for electrocardiographs Charles Ho, Lynn Braddock, Benjamin Eloff, Brian Lewis, Nina Nwaba, Linda Ricci, Estelle Russek-Cohen, Elias Mallis, (U.S. Food and Drug Administration, Rockville, Maryland, USA) Food and Drug Administration (FDA)/center for device and radiological health (CDRH) is charged with the responsibility to evaluate new electrocardiogram (ECG) devices for market clearance. As ECG technology expands into new diagnostic areas, FDA is challenged with evaluating the results of clinical studies that aim to support these new diagnostic claims. Because of the investment of time, effort, and other resources, FDA strongly encourages early collaboration with ECG developers before the initiation of a clinical study. In this article, we aim to describe some key considerations for an ECG developer to consider before planning and conducting clinical testing. These considerations may hopefully provide an efficient, least burdensome path toward market clearance. First and foremost is the context for use of the device and capturing the right patients and the right users of the device. Both the types of cases and the type of user (ie, level of training and experience) will depend on the setting of the study. This will drive expectations concerning performance. In planning a study, there is a need for stating the null and alternative hypotheses, and this is partially driven by contextual description of the hypotheses. We believe that a rigorous mathematical description of the null and alternative hypothesis will completely define the aim that the investigator wants to achieve. Furthermore, these hypotheses will directly impact the sample size and subsequently the length of study. The statistical analysis plan should also consider which statistical methods will be used to assess the hypotheses. Another issue to take into consideration is the prevalence of the disease in the population to be studied. In addition to impacting the availability of patients with the particular disease, the disease prevalence can impact the usefulness of commonly used statistical metrics such as sensitivity and specificity. For diseases with low prevalence in a prospective study, we