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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

Temporal scalability of ECG signal for lead II, which is the voltage between the (positive) left leg electrode and the right arm electrode, as the number of ELs increases, for the duration of one second. The maximum sampling frequency is 360 Hz; thus, the maximum number of available ELs is 3 (N = 3).
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Related In: Results  -  Collection


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fig4: Temporal scalability of ECG signal for lead II, which is the voltage between the (positive) left leg electrode and the right arm electrode, as the number of ELs increases, for the duration of one second. The maximum sampling frequency is 360 Hz; thus, the maximum number of available ELs is 3 (N = 3).

Mentions: Using the layered representation introduced above, Figure 4 shows that the quality of the ECG signal improves as more EL data are appended to the BL data, as expected. Further fine-grained scalability can be accomplished by increasing the maximum sampling frequency and the corresponding number of available ELs.


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

Kang K - ScientificWorldJournal (2014)

Temporal scalability of ECG signal for lead II, which is the voltage between the (positive) left leg electrode and the right arm electrode, as the number of ELs increases, for the duration of one second. The maximum sampling frequency is 360 Hz; thus, the maximum number of available ELs is 3 (N = 3).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig4: Temporal scalability of ECG signal for lead II, which is the voltage between the (positive) left leg electrode and the right arm electrode, as the number of ELs increases, for the duration of one second. The maximum sampling frequency is 360 Hz; thus, the maximum number of available ELs is 3 (N = 3).
Mentions: Using the layered representation introduced above, Figure 4 shows that the quality of the ECG signal improves as more EL data are appended to the BL data, as expected. Further fine-grained scalability can be accomplished by increasing the maximum sampling frequency and the corresponding number of available ELs.

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