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An adaptive framework for real-time ECG transmission in mobile environments.

Kang K - ScientificWorldJournal (2014)

Bottom Line: According to this observation, we have devised a simple and efficient real-time scheduling algorithm based on the earliest deadline first (EDF) policy, which decides the order of transmitting or retransmitting packets that contain ECG data at any given time for the delivery of scalable ECG data over a lossy channel.The algorithm takes into account the differing priorities of packets in each layer, which prevents the perceived quality of the reconstructed ECG signal from degrading abruptly as channel conditions worsen, while using the available bandwidth efficiently.Extensive simulations demonstrate this improvement in perceived quality.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science and Engineering, Hanyang University, Ansan 426-791, Republic of Korea.

ABSTRACT
Wireless electrocardiogram (ECG) monitoring involves the measurement of ECG signals and their timely transmission over wireless networks to remote healthcare professionals. However, fluctuations in wireless channel conditions pose quality-of-service challenges for real-time ECG monitoring services in a mobile environment. We present an adaptive framework for layered coding and transmission of ECG data that can cope with a time-varying wireless channel. The ECG is segmented into layers with differing importance with respect to the quality of the reconstructed signal. According to this observation, we have devised a simple and efficient real-time scheduling algorithm based on the earliest deadline first (EDF) policy, which decides the order of transmitting or retransmitting packets that contain ECG data at any given time for the delivery of scalable ECG data over a lossy channel. The algorithm takes into account the differing priorities of packets in each layer, which prevents the perceived quality of the reconstructed ECG signal from degrading abruptly as channel conditions worsen, while using the available bandwidth efficiently. Extensive simulations demonstrate this improvement in perceived quality.

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Related in: MedlinePlus

Snapshot of ECG signal fluctuations for MLII channel when PER is 0.1 and the patient moves at 2 km/h.
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Related In: Results  -  Collection


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fig10: Snapshot of ECG signal fluctuations for MLII channel when PER is 0.1 and the patient moves at 2 km/h.

Mentions: The relative advantage of our framework can clearly be seen in Figure 10, which depicts a snapshot of the original ECG signal obtained from patient and that of the corresponding signal reconstructed in the RMS under severe channel conditions (ϵ = 0.1 and v = 2 km/h). In such a situation, many residual erroneous packets often remain even after error control, both in our framework and the CTF. It is observed that compared to the original ECG signal in Figure 10(a), the ECG signal reconstructed with CTF frequently omits important ECG information; this might lead a physician to misinterpret a patient's condition. For example, the original ECG signal has 13 spikes, whereas the ECG signal reconstructed with CTF has only 11 spikes, as shown in Figure 10(b). As a result, in spite of the fact that the original ECG diagnosis is normal sinus rhythm with an atrial premature beat, the distorted reconstruction leads to a diagnosis that indicates sinus pause or sinoatrial block, which is a more serious problem. However, for the same pattern of error in the wireless channel, the perceived quality of the reconstructed signal degrades very gracefully in our framework, as shown in Figure 10(c), with the help of layered representation and by selectively recovering packets with higher priority. This provides the physician with a better chance of arriving at an accurate diagnosis.


An adaptive framework for real-time ECG transmission in mobile environments.

Kang K - ScientificWorldJournal (2014)

Snapshot of ECG signal fluctuations for MLII channel when PER is 0.1 and the patient moves at 2 km/h.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4109261&req=5

fig10: Snapshot of ECG signal fluctuations for MLII channel when PER is 0.1 and the patient moves at 2 km/h.
Mentions: The relative advantage of our framework can clearly be seen in Figure 10, which depicts a snapshot of the original ECG signal obtained from patient and that of the corresponding signal reconstructed in the RMS under severe channel conditions (ϵ = 0.1 and v = 2 km/h). In such a situation, many residual erroneous packets often remain even after error control, both in our framework and the CTF. It is observed that compared to the original ECG signal in Figure 10(a), the ECG signal reconstructed with CTF frequently omits important ECG information; this might lead a physician to misinterpret a patient's condition. For example, the original ECG signal has 13 spikes, whereas the ECG signal reconstructed with CTF has only 11 spikes, as shown in Figure 10(b). As a result, in spite of the fact that the original ECG diagnosis is normal sinus rhythm with an atrial premature beat, the distorted reconstruction leads to a diagnosis that indicates sinus pause or sinoatrial block, which is a more serious problem. However, for the same pattern of error in the wireless channel, the perceived quality of the reconstructed signal degrades very gracefully in our framework, as shown in Figure 10(c), with the help of layered representation and by selectively recovering packets with higher priority. This provides the physician with a better chance of arriving at an accurate diagnosis.

Bottom Line: According to this observation, we have devised a simple and efficient real-time scheduling algorithm based on the earliest deadline first (EDF) policy, which decides the order of transmitting or retransmitting packets that contain ECG data at any given time for the delivery of scalable ECG data over a lossy channel.The algorithm takes into account the differing priorities of packets in each layer, which prevents the perceived quality of the reconstructed ECG signal from degrading abruptly as channel conditions worsen, while using the available bandwidth efficiently.Extensive simulations demonstrate this improvement in perceived quality.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science and Engineering, Hanyang University, Ansan 426-791, Republic of Korea.

ABSTRACT
Wireless electrocardiogram (ECG) monitoring involves the measurement of ECG signals and their timely transmission over wireless networks to remote healthcare professionals. However, fluctuations in wireless channel conditions pose quality-of-service challenges for real-time ECG monitoring services in a mobile environment. We present an adaptive framework for layered coding and transmission of ECG data that can cope with a time-varying wireless channel. The ECG is segmented into layers with differing importance with respect to the quality of the reconstructed signal. According to this observation, we have devised a simple and efficient real-time scheduling algorithm based on the earliest deadline first (EDF) policy, which decides the order of transmitting or retransmitting packets that contain ECG data at any given time for the delivery of scalable ECG data over a lossy channel. The algorithm takes into account the differing priorities of packets in each layer, which prevents the perceived quality of the reconstructed ECG signal from degrading abruptly as channel conditions worsen, while using the available bandwidth efficiently. Extensive simulations demonstrate this improvement in perceived quality.

Show MeSH
Related in: MedlinePlus