You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: A Comprehensive Survey of MAC Protocols for Wireless Body Area Networks 
Chapter Title:  I. Heartbeat-Driven MAC Protocol 
1. Abstract of Cited Paper (BIB001):  Beat detection algorithms have many clinical applications including pulse oximetry, cardiac arrhythmia detection, and cardiac output monitoring. Most of these algorithms have been developed by medical device companies and are proprietary. Thus, researchers who wish to investigate pulse contour analysis must rely on manual annotations or develop their own algorithms. We designed an automatic detection algorithm for pressure signals that locates the first peak following each heart beat. This is called the percussion peak in intracranial pressure (ICP) signals and the systolic peak in arterial blood pressure (ABP) and pulse oximetry (SpO/sub 2/) signals. The algorithm incorporates a filter bank with variable cutoff frequencies, spectral estimates of the heart rate, rank-order nonlinear filters, and decision logic. We prospectively measured the performance of the algorithm compared to expert annotations of ICP, ABP, and SpO/sub 2/ signals acquired from pediatric intensive care unit patients. The algorithm achieved a sensitivity of 99.36% and positive predictivity of 98.43% on a dataset consisting of 42,539 beats. 
2. Abstract of Cited Paper (BIB002):  In this paper, a Body Sensor Network (BSN) based context aware QRS detection scheme is proposed. The algorithm uses the context information provided by the body sensor network to improve the QRS detection performance by dynamically selecting the leads with best SNR and taking advantage of the best features of two complementary detection algorithms. The accelerometer data from the BSN are used to classify the patients' daily activity and provide the context information. The classification results indicate both the type of the activities and their corresponding intensity, which is related to the signal/noise ratio of the ECG recordings. Activity intensity is first fed to lead selector to eliminate the leads with low SNR, and then is fed to a selector for selecting a proper QRS detector according to the noise level. MIT-BIH noise stress test database is used to evaluate the algorithms. 
3. Abstract of Cited Paper (BIB003):  H-MAC is a novel Time Division Multiple Access (TDMA) based MAC protocol designed for Body Sensor Networks (BSNs). It improves energy efficiency by exploiting human heartbeat rhythm information to perform time synchronization for TDMA. Heartbeat rhythm is inherent in every human body and can be detected in a variety of biosignals. Therefore, biosensors in BSNs can extract the heartbeat rhythm from their sensory data. Moreover, all the rhythms represented by peak sequences are naturally synchronized since they are driven by the same source, the heartbeat. By following the rhythm, wireless biosensors can achieve time synchronization without having to turn on their radio to receive periodic timing information from a central controller, so that energy cost for time synchronization can be completely avoided and the lifetime of network can be prolonged. An active synchronization recovery scheme is also developed, in which two resynchronization procedures are implemented. The algorithms are verified using real world data from MIT-BIH multi-parameter database MIMIC.

Literature Review:
In BIB004 , authors propose a TDMA based protocol for WBANs with utilization of Heartbeat-Rhythm for synchronization. Network topology for proposed protocol is star topology where a central node coordinates network. To avoid collision, H-MAC assigns dedicated time slots to sensor nodes for communication. Using Heartbeat Rhythm, H-MAC maintains synchronization required for TDMA approach without using periodic control messages. This mechanism leads to minimize overall energy consumption. Each biosensor extracts Heartbeat Rhythm information from its sensory data. For detection of peaks in the heartbeat rhythm, authors use the algorithms proposed in BIB003 BIB002 . Synchronization is archived by these peaks. H-MAC uses the peek intervals for data communication. Time slots assignment and frame cycles for synchronization are calculated by coordinator. Coordinator also utilizes Heartbeat Rhythm information from its own sensory data. From simulation results, H-MAC prolongs networks life as compared to Lightweight MAC (L-MAC)  and Sensor MAC (S-MAC) BIB001 . This efficiency is achieved by TDMA approach, where collisions are avoided by dedicated time slots and reduced idle listening. Replacement of traditional synchronization with Heartbeat Rhythm pattern also reduces energy consumption. However, Heartbeat Rhythm is not accessible by all sensors like accelerometer. In such cases devices can not by synchronized. Integration of accelerometer with other sensors or facilitating accelerometer to access heartbeat leads to complexity. Similarly insertion of guard band time to avoid collisions leads to minimize bandwidth utilization.