Development of Computer Aided Prediction Technology for Paroxysmal Atrial Fibrillation in Mobile Healthcare (Cardiac Arrhythmias) Part 3

Prediction of AF recurrence using 48-h recordings

ECG data from 48-h Holter monitoring were first screened for AF episodes using the software in the Holter system and software findings for AF were confirmed by two cardiologists (JHP, JHK). Nineteen of 33 subjects were diagnosed as NSR whose heart rhythms were successfully maintained for 48 h under drug treatment (normal group). Ten subjects were diagnosed as PAF and four as persistent AF (recurrent group; recurrence rate of 42%). Normal and recurrent groups did not differ significantly different in terms of age (Mann-Whitney test, p > 0.05), gender, or other diseases such as diabetes, hypertension, or past strokes (chi-squared test, p > 0.05).

The noise detection algorithm detected four NSR cases and showed that more than 30% of the total ECG data were corrupted by noise possibly due to poor electrode contacts; these were removed from the test sets to avoid misinterpretation. When four ECG segments were sampled from each time period of ECG data, a total of 350 and 270 segments were obtained from 15 normal and 14 recurrent cases, respectively, and were processed for the calculation of HRV and Poincare plot features. Using the segment-based classification and subject-based rule, 9/10 recurrent PAF patients were correctly identified, whereas 13/15 normal subjects were correctly identified (data not shown). In addition, four persistent AF cases were all correctly identified. An illustrative example for the subject-based rule is described in Figure 5, in which a data point represents the probability density value of the segment based model at a given time-point. Data points for an NSR case remain higher than the empirical cut-off at all time-points, whereas those for a PAF case often fall below the cut-off even during the days when no episodes were evident (Figure 5). Subjects classified as normal cases by the subject-based rule (n = 14) were further tested with the CR-based model. One false-negative case was correctly identified as recurrent and all normal cases were correctly confirmed as normal cases. Therefore, the final classification resulted in a sensitivity of 100% (14/14 recurrent cases) and a specificity of 86% (13/15 normal cases) (Table 4). The same classification procedure was applied to the data sets obtained by sampling two or three segments per time period and classification results are summarized in Table 4. The number of sampled segments did not change the classification outcomes drastically.

Conclusion and discussion

In this study, we have developed a new method for predicting PAF subjects using intermittently sampled ECG data and applied it to the identification of recurrent AF cases. The proposed method consists of an empirical rule and a CR-based classification. The empirical rule alone identified nearly 93% of recurrent cases (13/14 cases) and 86% of normal cases (13/15 cases). Because our aim was not to miss any recurrent cases, normal cases classified by the empirical rule were re-tested using the CR-based prediction model. The false-negative case was correctly identified as a recurrent case thus achieving a 100% sensitivity and no false-positive cases were generated thus maintaining the 86% specificity. Our results suggest that intermittently sampled ECG data could be used to detect the increased likelihood of PAF episodes. Since previous PAF prediction methods relied on the change during the transition from NSR to PAF, ECG needs to be analyzed continuously not to miss transition periods. Furthermore, somewhat higher degree of false positive errors may be expected in an actual implementation of long term ECG analysis since the likelihood of not having subsequent PAF episodes following a transition period is not known.

Contrary to previous prediction methods, our classification models were not designed to detect the transition period prior to PAF episodes exclusively. Instead, they were aimed to evaluate the likelihood of a 24 hour period when PAF episodes may occur. Thus, the performance of our proposed method can be less sensitive to the continuity of ECG data but allows ECG recordings to be sampled over the whole day. Two samples gave results that were as accurate as those of four samples taken during four 6-h time periods (Table 4). Furthermore, our results indicated that the proposed methods tended to show an increased likelihood of detecting PAF cases even during the days when no PAF episodes were evident (Figure 5). Therefore, we conclude that our intermittent sampling strategy is as accurate for predicting recurrent PAF as previously reported methods.

Table 4. Performance of proposed methods for predicting PAF subjects using a subject-based rule and a CR-based classification algorithm. Performance results were obtained when two, three, or four 30-min ECG records were sampled from each of four time periods during a day.

The intermittent sampling approach might be more effective in mobile healthcare settings because the sensor may not need to be worn all day, which could effectively reduce many problems caused by poor sensor tolerance, limited battery life, and high data transmission costs of current technology. In general, mobile healthcare technology offers many attractive features such as convenient wearable ECG sensors, real-time feedback of abnormal heart rhythms, and timely intervention in the case of adverse events. However, the continuous measurement of ECG signals might not be ideal as a long-term monitoring solution in mobile healthcare settings because it requires long hours of wearing a sensor that may stigmatize a majority of patients and eventually influence the quality of the data. For example, Holter monitoring was regarded as inconvenient because of hygienic aspects, physical activity, night sleep, and skin reactions (Fensli & Boisen, 2009). Based on our understanding of current advances in low-power bioelectronics (Sarpeshkar, 2010), the proposed intermittent sampling of ECG signals is believed to provide an attractive alternative strategy to long-term monitoring in mobile healthcare settings. It could be specially adapted to work with wireless devices such as wearable sensors and gateway devices that consume battery power at high rates. In addition, the amount of data traffic would be minimized, which is also attractive in countries where wireless data transfer is costly. Thus, in developing a computer-aided prediction method for mobile healthcare settings, it seems important to consider human factors, such as patient acceptance of procedures, or device factors, such as battery time or data transfer costs.

Fig. 5. Distribution of probability values from the segment-based model applied to two patient cases. Data points in the hollow circle are from a normal case (NSR). Data points in the solid square are from a PAF case on the day when PAF episodes were recorded, whereas those in the hollow square and triangle are from the same case during the previous and following days when no PAF episodes were recorded. The points in the area under the cut-off at 0.25 (broken line) were classified as PAF.

It is becoming evident that a significant proportion of cryptogenic stroke is due to intermittent AF. By using 30-day cardiac event monitors, 20% of such strokes was found to be related to AF (Elijovich et al., 2009). Warfarin treatment was given to patients after the detection of intermittent AF (despite no detection of AF on ECG or in-patient telemetry monitoring in the majority of patients). Similarly to the detection of recurrent PAF, prevention of recurrent strokes related to PAF in particular may require long-term ECG monitoring. For these applications, our proposed intermittent sampling method should also be suitable as an initial screening method that generates a real-time alarm or trend report that enables timely intervention. For example, if the incidence of abnormal segments increases, then the ECG sampling strategy may be changed to a continuous monitoring mode to capture the PAF episodes. In this way, patients can be monitored in the long term to determine recurrence after conversion treatment or the origin of strokes, so that conventional Holter monitoring can be complemented or improved. For initial screening purposes, the ECG sensor used in this study can be replaced by a heartbeat sensor because all analytic features were calculated from RR interval data rather than the morphology of ECG signals. Current advances in microelectronics have provided a variety of heartbeat sensors ranging from conventional chest belt type to Doppler radar-based non-contact types. These sensors are usually equipped with a module of wireless data communication so they can transmit the signals to the gateway device that is connected to the internet.

Fig. 6. (A) System components of web-based heartbeat analysis system. (B) Electrode attachment. (C) Poincare plots were generated from wirelessly transmitted ECG data obtained from an NSR and an AF subject (Pictures in (A) and (B) were kindly provided by Alivetec Technologies Pty. Ltd.).

Development of web-based real-time heartbeat analysis

Currently, we are actively developing a remote real-time heartbeat analysis system that consists of a wearable ECG sensor (AliveECG monitor, Alive Technologies Pty. Ltd, Ashmore, Queensland, Australia) with a three-axis accelerometer, a smart phone (HD2, HTC Corporation, Taoyuan, Taiwan ROC), and a data analysis server (Microsoft Windows XP; Figure 6). The original server software (Cardiomobile, Alive Technologies) displays ECG, activity features, and global positioning system-based location in a web-based map in real time or in review mode. In addition to these functions, HRV features and outputs from a PAF prediction model are also calculated and displayed in our current version (Figure 7). A feasible scenario of using our system is that a wireless ECG sensor worn by a patient, and communicating to the smart phone through the near field Bluetooth, transmits ECG signals to the remote server through a wireless data streaming service. The remote server performs real-time analysis of transmitted ECG data and detects abnormal events (or trends). In the case of PAF prediction, a cardiology specialist at the hospital may be notified to interpret the remote monitoring and confirm the diagnosis. Compared with conventional Holter monitoring systems, a mobile healthcare solution can be designed to enable the patient to carry out normal daily activities, while still being under continuous monitoring.

Fig. 7. Real-time heartbeat analysis system for remote monitoring of ECG and HRV of patients. Heart rates, energy expenditure, physical activity, HRV (RMSSD), and abnormal heartbeats (Abnormality %) can be monitored by remote clinical staff in real time while a patient is exercising outdoors during daily activity. In the case of advent events, user location data from the global positioning system can be provided to paramedic staff (user’s current position is indicated by the avatar on the web-based map) (Cardiomobile is the trademark of Alivetec Technologies Pty. Ltd. The server software was kindly provided to be modified by us.)

Since the patient may experience an arrhythmic episode during physical activity, mobile solutions may enhance the quality of data measured by enabling the patient to carry out normal daily routines. Currently, we are also implementing the CR analysis of HRV features and its related PAF prediction. This prototype system should help us to discover the "real problem" and the users’ requirements, demonstrate the actual functionality of a device, and provide many insights on how to design and build a more advanced system that should enable long-term ECG monitoring. The future system is being designed to provide additional benefits for stroke or heart disease rehabilitation patients.