Miniaturized Wireless ECG Monitor for Real-Time Detection of Epileptic Seizures FABIEN MASSE´ , Holst Center/imec MARTIEN VAN BUSSEL, Kempenhaeghe/Hobo Heeze B.V. ALINE SERTEYN, TU Eindhoven/Signal Processing Systems JOHAN ARENDS, Kempenhaeghe/Hobo Heeze B.V. JULIEN PENDERS, Holst Center/imec
Recent advances in miniaturization of ultra-low power components allow for more intelligent wearable health monitors. The development and evaluation of a wireless wearable electrocardiogram (ECG) monitor to detect epileptic seizures from changes in the cardiac rhythm is described. The ECG data are analyzed by embedded algorithms: a robust beat-detection algorithm combined with a real-time epileptic seizure detector. In its current implementation, the proposed prototype is 52 × 36 × 15mm3 , and has an autonomy of one day. Based on data collected on the first three epilepsy patients, preliminary clinical results are provided. Wireless, miniaturized and comfortable, this prototype opens new perspectives for health monitoring. Categories and Subject Descriptors: J.3 [Life Medical Science]: Health General Terms: Algorithms, Measurement, Experimentation, Human Factors Additional Key Words and Phrases: Body area network, epilepsy, real-time, wireless, ECG ACM Reference Format: Mass´e, F., van Bussel, M., Serteyn, A., Arends, J., and Penders, J. 2013. Miniaturized wireless ECG monitor for real-time detection of epileptic seizures. ACM Trans. Embedd. Comput. Syst. 12, 4, Article 102 (June 2013), 21 pages. DOI: http://dx.doi.org/10.1145/2485984.2485990
1. INTRODUCTION
Epilepsy is a chronic neurological disorder that affects more than 50 million people worldwide (prevalence 5–10 per 1000 people [Sander 2003]). It is characterized by recurrent unprovoked events, called seizures, which are symptomatic of abnormal brain activity. There are many different epilepsy syndromes and seizure types; ranging from mild, hardly noticeable to severe, life-threatening seizures. Prescription of pharmaceuticals (antiepileptics) is the primary treatment by which seizures are successfully suppressed in the majority of patients. Approximately 30% of the epilepsy patient population has refractory epilepsy; they still suffer from seizures despite receiving the best possible treatment. To improve the quality of care and diagnosis, long-term electroencephalogram (EEG) monitoring in combination with video is frequently performed. It consists in placing up to 100 gel electrodes on the scalp. EEGvideo monitoring is not well-suited for monitoring patients in their daily-life activities. Authors’ addresses: F. Mass´e and J. Penders, Holst Center/imec, High Tech Campus 31, 5656AE Eindhoven, Netherlands; email:
[email protected]; A. Serteyn, Signal Processing Systems, TU Eindhoven, Den Dolech 2, 5612AZ Eindhoven, Netherlands; M. van Bussel and J. Arends, Kempenhaeghe/Hobo Heeze B. V., Sterkselseweg 65, 5591VE. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies show this notice on the first page or initial screen of a display along with the full citation. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, to republish, to post on servers, to redistribute to lists, or to use any component of this work in other works requires prior specific permission and/or a fee. Permissions may be requested from Publications Dept., ACM, Inc., 2 Penn Plaza, Suite 701, New York, NY 10121-0701 USA, fax +1 (212) 869-0481, or
[email protected]. c 2013 ACM 1539-9087/2013/06-ART102 $15.00 DOI: http://dx.doi.org/10.1145/2485984.2485990 ACM Transactions on Embedded Computing Systems, Vol. 12, No. 4, Article 102, Publication date: June 2013.
102
102:2
F. Masse´ et al.
Such monitoring systems are also difficult to set up as they require highly trained (expensive) personnel. Moreover, the analysis of the huge amount of data is laborious. Recently, wireless ambulatory EEG devices for long-term epilepsy monitoring and real-time seizure detection have been developed [Casson and Rodriguez-Villegas 2009; Raghunathan et al. 2009; Waterhouse et al. 2003; Patel et al. 2009]. However, the current ambulatory EEG systems encounter several limitations, such as device size, weight, and unreliable electrodes attachment to scalp [Casson et al. 2008]. Because the electrodes are placed on the head, this may also not be socially accepted for dailylife use. An online continuous seizure detector packaged in a comfortable, unobtrusive and wearable device would allow diagnosis in a simpler way. It would also enhance patient’s safety by triggering alarm notifications in case of major life-threatening seizures during daily activities. The challenging issue of detecting epileptic seizures in real-time while not impairing patient’s daily-life calls for alternative ways of detection [Nijsen et al. 2005]. The seizures express themselves in various physiological changes, relating to the activated part of the brain and the level of excitation of the seizure. Cardiac regulation is largely determined by the sympathetic and parasympathetic part of the nervous system (SaPS). When the corresponding parts of the brain are influenced by seizure activity, cardiac abnormalities such as changes in the heart rhythm (sinus tachychardia or bradycardia), ST-depression, or T-wave inversion may occur [Blumhardt et al. 1986; Epstein et al. 1992; Zijlmans et al. 2002; Opherk et al. 2002; Leutmezer et al. 2003]. The focus of the current study is to detect heart-rate (HR) changes related to epileptic seizures in real-time using a wearable cardiac monitor. Leutmezer et al. [2003] reflected that, in 110 seizures out of 145, ictal heart-rate changes preceded the onset of the surface EEG by a few seconds. This is attributed to early seizure related activation of the brain cells involved in the SaPS. The proposed system is intended to alert a caregiver (or relative, friend, etc.) in case of a potentially dangerous seizure. It also logs occurrences of seizures for a more accurate therapy monitoring as according to a study conducted on 30 patients [Kerling et al. 2006], 44% of the epileptic seizures remain unnoticed by the patient (61% if the seizure occurs during sleep). It is however not meant to predict seizures as the time between detected changes in cardiac activity and the clinical onset of the seizure is too short to warn the wearer and allow him/her to take preventive measures, for instance, to sit down, stop a car, etc. Also, currently no proven solutions, such as medication or electrical stimulation, exist that can counter a beginning seizure. Wireless electrocardiogram (ECG) monitoring has always manifested a strong and ever increasing interest by the research community. By enabling unobtrusive monitoring and providing increased mobility, it broadens the scope of possible heart condition monitoring applications [Yazicioglu et al. 2009], including epileptic seizure detection (ESD) [Van Elmpt et al. 2006]. Most of the current ECG-monitors focus either on increasing the number of functionalities [Kyriacou et al. 2007] or on reducing the power consumption and miniaturizing the form factor, leaving the signal processing to a computer or a handheld unit [Massot et al. 2009; Chulsung et al. 2006]. Often, the embedded beat detection algorithm that is the ground layer for every heart rate-based analysis, is not well-characterized and therefore may not be a reliable basis for the analysis as in [Kyriacou et al. 2007] and [Chulsung et al. 2006]. Although recent studies reported online epileptic seizure detection based on body motions only [Lockman et al. 2011] or in combination with EMG [Bonnet et al. 2011], they presented either a large number of false positives or were validated on simulated epileptic seizures in lab conditions. To the best of the authors’ knowledge, none of them was doing so by analyzing the cardiac activity. This article presents the first ECG-based epileptic seizure detector and contributes to the state-of-the-art as follows: ACM Transactions on Embedded Computing Systems, Vol. 12, No. 4, Article 102, Publication date: June 2013.
Miniaturized Wireless ECG Monitor for Real-Time Detection of Epileptic Seizures
102:3
(1) The design and characterization of a miniaturized, wireless, and wearable ECGmonitor that supports extended functionalities for facilitating the data collection (raw data storage, real-time visualization, and synchronization with external clinical equipment). (2) The development of real-time algorithms capable of monitoring major epileptic seizures (tonic-clonic, generalized tonic, clonic or hypermotor) based on variations of the HR, and their implementation in the wearable ECG-monitor. (3) The validation of the designed prototype in a small pilot study including healthy volunteers and epilepsy patients. This article is organized as follows. First, the requirements from the clinical field are described. Then, the low-power and miniaturized prototype is detailed as well as the embedded algorithms required to perform epileptic seizure detection. The wireless communication services that ensure reliable epilepsy-related event transmission are also presented along with the software architecture. Additionally, the epilepsy detector is characterized in terms of memory footprint, power consumption, and real-time functionality. Finally, the results of the data collection performed on healthy volunteers and three epilepsy patients with the system are provided. 2. CLINICAL REQUIREMENTS
Patient’s acceptance is key for introducing a device into clinical practice. First, the system shall not impair patient’s daily-life activities and therefore be as unobtrusive as possible (Req #1). The position of the device on the body shall be comfortable during both daytime and night, and safe (Req #2) in case of a major seizure while still ensuring a “high” ECG signal quality. The ECG signal quality is qualified as high when during periods of heavy body motions, such as major seizures, the R-peaks remain visible and can be further processed. Such requirements also enable more accurate diagnosis as the patient will not feel disturbed by the device. The system packaging shall be robust (Req #3) enough to be suitable for monitoring epilepsy patients who may be severely physically and/or mentally challenged. For similar reasons, user’s interactions (LED, button, etc. . .) at the device level (Req #4) shall also be limited to avoid unwanted sensor behavior (e.g., sensor shut down) during seizures. The system shall also be easy-to-use and convenient for the clinician (Req #5) during manipulation of the patient. The resulting prototype shall also be in compliance with current electrical safety standards [ANSI 2001] (Req #6), in particular those relevant for Kempenhaeghe’s ethical committee. To alert the nursing staff about a potential seizure, the ECG signal shall be acquired and analyzed (Req #7) in real time with a particular focus on coping with motion artifacts. It shall also be able to communicate the outcome of the real-time processing algorithms to a control unit (Req #8). At the same time, the raw ECG signal as well as the algorithm outputs should be logged (Req #9) for further offline analysis. Synchronization (Req #10) of the ECG system with external equipment such as a digital video system is also required for the purpose of this study. To improve the flexibility of the system and facilitate the data collection, an exchange of information between the operator and the body-worn ECG sensor device must be possible. The control unit shall enable the clinician to visualize (Req #11) the raw ECG data for accurate electrode placement as well as the general status of the system (battery level, run time, and memory status). Moreover, the clinical staff shall also be able to annotate the data collection while running (Req #12) and tune algorithm parameters (Req #13). Finally, to reduce the burden on the nursing staff of swapping and setting up the device, the system should allow at least 24 hours of autonomy during continuous ACM Transactions on Embedded Computing Systems, Vol. 12, No. 4, Article 102, Publication date: June 2013.
102:4
F. Masse´ et al. Table I. ECG Monitor: Clinical Requirements
# Requirements Physical 1 Unobtrusive 2 Safe position 3 Robust 4 Limited hardware inputs/outputs 5 Easy-to-setup 6 Electrically safe
Timing 7 Real-time ECG acquisition and processing Functional 8 Reporting outcome of ECG processing 9 Data logging 10 Synchronization with external clinical equipment User interface 11 Visualization of raw ECG data and device status 12 Online annotation 13 Online parameter tuning Power 14 Autonomy
Purpose Patient’s acceptance and more accurate diagnosis Patient’s safety Suitability for epilepsy patient Suitability for epilepsy patients Convenience for clinicians Compliance with current electronic safety standards for approval by the Kempenhaeghe’s ethical committee Online epilepsy seizure detection
Notification about a potential epileptic seizure Offline processing of the data Notification about a potential epileptic seizure through the clinical software used in daily practice - Facilitated electrodes/sensor placement - Display of the device health status Annotation of events occurring during data collection Upload of new algorithm parameters Acceptance by both patient and clinical staff
monitoring (Req #14). It could then be recharged once a day, for instance while the patient is having a shower, or replaced before going to bed. The requirements are summarized in Table I. 3. SYSTEM DESCRIPTION
Wearable technologies combined with wireless telemetry allow for unobtrusive (Req #1) patient monitoring by enabling freedom of movement to the patient in daily-life environment. First, the system architecture section translates the clinical requirements into technical requirements and presents the interactions between the different system components. Second, the packaging robustness, the miniaturization process and the main electronic components are described. Third, the heartbeat and the epilepsy seizure detection algorithms are presented. Then the wireless communication protocol is overviewed. Finally, the firmware and software implementations are detailed. 3.1. System Architecture
The complete data acquisition platform comprises the ECG monitor and a control unit representing a dedicated device, such as a computer. Real-time analysis of the ECG data (Req #7) is achieved by processing the data stream locally on the ECG monitor. The host application serves only as a graphical (Req #11/12) and/or network interface for the ECG monitor since most of the intelligence (signal processing tasks) are offloaded to the ECG monitor (Req #8). It supports communication with third-party software (Req #10), for instance, a TCP/IP connection with another clinical monitoring application providing extended functionalities. The diagram in Figure 1 illustrates the overall data acquisition architecture. ACM Transactions on Embedded Computing Systems, Vol. 12, No. 4, Article 102, Publication date: June 2013.
Miniaturized Wireless ECG Monitor for Real-Time Detection of Epileptic Seizures
102:5
Fig. 1. Architecture overview. The control unit acts as a gateway between the external clinical application and the ECG monitor that samples, processes, and stores the data. Table II. ECG monitor: main components and their current consumptions. ∗ SD card current consumption is devicedependent (not only manufacturer-dependent). Type MCU
Part name TI MSP430F1611
Main features 10 KB RAM/48 KB ROM
Radio
nRF24L01
2.4 GHz/2Mbps
ECG sensor Accel. MicroSD
[Yazicioglu,2007] ADXL330 SanDisk Mobile
CMMR: 120dB −/+ 3g 2 GB
Current consumption (at 3V) Active: 4mA @ 8MHz Sleep: 700 uA (Mode 1) Reception: 13 mA Transmission: 11 mA Sleep: 220 uA 21 uA 320 uA Write/read:
