ECG monitoring systems have a significant role in detecting cardiovascular diseases and reducing the rate of sudden cardiac deaths. One of the critical factors to support real-time ECG tracking is to guarantee monitoring system availability. Hence, this work targets battery life expansion for a 12 Lead ECG patch. ECG patch operational hours are extended by reducing Bluetooth Low Energy (BLE) communication airtime, hence reducing the overall transmission power and extending the battery life. Huffman, delta, and base-delta compression techniques are implemented on a Texas Instruments CC2650 Microcontroller Unit using different sampling rates and cardiac conditions such as normal, ventricular tachycardia, and ventricular fibrillation state. The performance of each encoding algorithm is evaluated in terms of compression ratio, the execution time, and power consumption of the ECG patch. Our findings show that the base-delta encoding technique outperforms other techniques and achieves 70% data compression on normal ECG data, 41% on ventricular fibrillation, and 44% on ventricular tachycardia. The execution time of base-delta encoding takes less than 25 ms execution time and saves up to 36 % of the power consumption on the MCU environment.

In response to the rapid digital revolution and the COVID-19 pandemic, the healthcare landscape has significantly shifted from physical to virtual care and telemedicine. As a result, healthcare providers and patients have shown increased interest and adoption for up-to-date technologies to monitor ongoing health conditions, including cardiovascular diseases. Driven by the importance of an efficient remote cardiovascular monitor for virtual care, we present a platform that enables remote ECG testing and provides ubiquitous data access to patients and their healthcare providers. A patent-pending 12-lead data acquisition ECG patch is attached to the patient's body to simultaneously collect heart signals, perform binary classification, and transmit the data to healthcare providers for further analysis at a high rate of up to 480 samples per second. As a preliminary classification phase, the presented platform introduces a machine learning technique to classify ECG signals near the ECG patch. The classification function is optimized for power-constrained devices using machine learning techniques. Moreover, the preliminary results of the energy consumption profile show that the ECG patch provides up to 37 hours of continuous 12-lead ECG streaming.

This paper explores ways for energy consumption reduction in wearable and Remote Patient Monitoring (RPM) devices. We use the XBeats ECG patch as a case study application for remote Electrocardiogram (ECG) wearable device power consumption benchmarking. Systematic energy consumption profiling criteria is proposed for evaluating participating components in an RPM device. We isolate each hardware component to find power-intensive processes in the XBeats system, discover energy consumption patterns, and measure voltage, current, power, and energy consumption for a given time period. The proposed optimization techniques demonstrate significant improvements to the hardware components on the ECG patch. The results show that optimizing the data acquisition process saves 8.2% compared to the original power consumption and 1.62% in data transmission over BLE, thus extending the device lifetime. Lastly, we optimize the data logging operation to save 54% of data initially written to an external drive.

We consider a communication setup where two source streams with different decoding deadlines, must be simultaneously transmitted over a single channel subjected to burst erasures. The encoder multiplexes the two source streams into a single stream of channel-packets. The decoder must recover the two source streams sequentially by their corresponding deadlines. One of the streams, the urgent stream, has a smaller delay than the other stream. We study the capacity region for such a setting for a certain range of system parameters. We divide the system into three different cases based on the relative values of the delays. For each case we provide achievability and converse bounds, which match under certain conditions. Our proposed coding scheme involves a careful construction of the parity check packets by jointly coding across the two streams despite different deadlines. Interestingly it is possible to transmit the urgent stream at a certain positive rate even when the sum rate equals the capacity associated with the less urgent stream. A separation based approach where we apply separate single-stream codes to each stream is suboptimal. Although our capacity results assume a simplistic channel model with a single erasure burst, we further demonstrate that our proposed code constructions also provide significant performance gains in simulations over statistical channel models with random bursts.

The burst rank loss network is an extension of the burst erasure channel, where the channel matrix between the sender and receiver becomes rank-deficient for a certain period of consecutive time-slots. We study streaming communication over the burst rank loss channel, propose a new class of codes, ROBIN codes, and establish their optimality. Our construction uses the maximum rank distance and maximum sum rank codes from previous works as constituent codes and combines them in a layered fashion. Our results generalize previous works on both the single-link and multiple-parallel-link streaming setups over burst erasure channels. We perform simulations over statistical network models to show that ROBIN codes attain low packet loss rates in comparison with the existing codes.

We introduce a new class of forward error correction (FEC) codes for VoIP communications which support different recovery delay depending on the channel conditions. Specifically, our proposed class of Dual-Delay (DD) codes can recover from challenging long bursts of losses with close to theoretical minimum delay so as to meet playback deadlines for recovered packets. They further improve conversational interactivity by achieving lower recovery delay during periods of random isolated losses. These DD codes are shown to achieve lower residual loss rates when compared to existing codes over a wide range of parameters of the Gilbert-Elliott channel. Experiments over real world packet traces further show performance gains of DD codes in terms of perceptually motivated ITU-T G.107 E-model.

We study a setting where two source streams with different decoding deadlines must be transmitted over a burst erasure channel. The source streams are multiplexed into a single stream of channel-packets at the encoder and transmitted over a packet erasure channel. The decoder must recover the source-packets within each stream sequentially, by their corresponding deadlines. We consider the burst-erasure channel model and characterize the capacity region for a certain range of system parameters. We show that the operation of the system can be divided into three different regimes based on the relative values of decoding deadlines. On the achievability side, we show that jointly coding across the two streams, despite their different deadlines, is necessary to achieve capacity. On the converse side we develop information theoretic outer bounds on the capacity region. We find that the capacity region exhibits a “corner point” where we can transmit the urgent stream at a positive rate, yet attain a sum-rate equal to the capacity of the non-urgent stream.

We study error correction codes for multimedia streaming applications where a stream of source packets must be transmitted in real-time, with in-order decoding, and strict delay constraints. In our setup, the encoder observes a stream of source packets in a sequential fashion, and M channel packets must be transmitted between the arrival of successive source packets. Each channel packet can depend on all the source packets observed up to and including that time, but not on any future source packets. The decoder must reconstruct the source stream with a delay of T packets. We consider a class of packet erasure channels with burst and isolated erasures, where the erasure patterns are locally constrained. Our proposed model provides a tractable approximation to statistical models, such as the Gilbert-Elliott channel, for capacity analysis. When M = 1, i.e., when the source-packet arrival and channel-packet transmission rates are equal, we establish upper and lower bounds on the capacity, that are within one unit of the decoding delay T. We also establish necessary and sufficient conditions on the column distance and column span of a convolutional code to be feasible, and in turn establish a fundamental tradeoff between these. Our proposed codes-maximum distance and span codes- achieve a near-optimal tradeoff between the column distance and column span, and involve a layered construction. When M > 1, we establish the capacity for the burst-erasure channel and an achievable rate in the general case. Extensive numerical simulations over Gilbert-Elliott and Fritchman channel models suggest that our codes also achieve significant gains in the residual loss probability over statistical channel models.

It is well known that dispersed and burst packet losses introduce significantly different amount of distortions. Since perceptual models are typically content dependent, it is challenging to characterize how losses interact with concealment. This paper presents loss-pattern-aware distortion (LoPAD), a content-independent metric that explicitly models the impact of different loss patterns. LoPAD operates solely on the loss trace without analyzing received media. It is fast, and supports offline and cloud-based monitoring of network impairment. Taking audio conferencing as target application, we show that full-reference PESQ scores for a collection of speech samples can be closely approximated by LoPAD. For various combinations of erasure channel models and forward error correction (FEC) codes, the correlation coefficients between LoPAD and PESQ-DMOS range from 0.90 to 0.97.

In the classic burst erasure channel, packets are consecutively erased in bursts between gaps of perfect communication. For the Burst Rank Loss Network, instead of a single-link erasure channel, there is a channel matrix that is rank-deficient for a burst of time before returning to full-rank. We establish the streaming capacity of the Burst Rank Loss Network and construct a new family of layered codes referred to as Recovery Of Bursts In Networks (ROBIN) codes that achieve capacity. Our results generalize previous work on both the single-link and multiple-parallel-link streaming setups. Simulations over statistical network models show that ROBIN codes lose fewer packets than baseline codes.

The column Hamming distance of a convolutional code determines the error correction capability when streaming over a class of packet erasure channels. We introduce a metric known as the column sum rank that parallels the column Hamming distance when streaming over a network with link failures. We prove the rank analogues of several known column Hamming distance properties and introduce a new family of convolutional codes that maximize the column sum rank up to the code memory. Our construction involves finding a class of super-regular matrices that preserve this property after multiplication with non-singular block diagonal matrices in the ground field.

We study low-delay streaming codes for erasure channels in point-to-point and multicast scenarios. We consider a sliding window erasure channel which captures the temporal correlation in packet losses observed in real channels. This correlation is often modelled using statistical channels such as Gilbert-Elliott channel. In the point-to-point case, we provide a new class of codes, Embedded Maximum Distance Separable (EMDS) codes, which recovers from channels introducing a mixture of burst and isolated erasures. Moreover, we propose a technique that extends point-to-point codes for the multicast scenario with two receivers that tolerate different delays, T1and T2. The multicast codes opportunistically decode packets with short delay T1when the channel is relatively better and with long delay T2when the channel is worse. Simulations over multicast Gilbert-Elliott channels show that EMDS codes outperform other streaming codes for both users.

The column Hamming distance of a convolutional code determines the error correction capability when streaming over a class of packet erasure channels. We show that the column sum rank parallels column Hamming distance when streaming over a network with link failures. We prove rank analogues of several column distance properties and introduce a new family of convolutional codes that maximize the column sum rank up to the code memory. Our construction involves finding a class of super-regular matrices that preserve this property after multiplication with non-singular block diagonal matrices in the ground field.

We study low-delay erasure correction codes in a real-time streaming setup. The encoder observes a stream of source packets and outputs the channel packets in a causal fashion, which are broadcast to two receivers over burst-erasure channels. Each receiver must decode the source packets sequentially with a deadline of Ti, while its channel can introduce an erasure burst of maximum length Bi, where i ∈ {1,2} and w.l.o.g. B2> B1. We study the associated capacity as a function of the burst lengths and decoding deadlines. We observe that the operation of the system can be divided into two main regimes. The so-called large-delay regime corresponds to the case when either T1≥ B2or T2≥ B1+ B2. We show that for these parameters, the optimal code is obtained through simple modifications of previously proposed single-user codes by Martinian et al. and the diversity embedded streaming codes proposed by Badr et al. When both T12and T21+ B2, the system is said to be in the low-delay regime. We propose a new code construction and establish its optimality when T2≥ T1+ B1. In the case when T21+ B1, we establish upper and lower bounds on the capacity and characterize the exact capacity when either T1= B1or T2= B2. Our upper bounds in the low-delay regime are based on novel information theoretic arguments that capture the tension between the decoding constraints at the two receivers.

We study low-delay error correction codes for streaming recovery over a class of packet-erasure channels that introduce both burst-erasures and isolated erasures. We propose a simple, yet effective class of codes whose parameters can be tuned to obtain a tradeoff between the capability to correct burst and isolated erasures. Our construction generalizes previously proposed low-delay codes which are effective only against burst erasures. We establish an information theoretic upper bound on the capability of any code to simultaneously correct burst and isolated erasures and show that our proposed constructions meet the upper bound in some special cases. We discuss the operational significance of column-distance and column-span metrics and establish that the rate 1/2 codes discovered by Martinian and Sundberg [IT Trans. 2004] through a computer search indeed attain the optimal column-distance and column-span tradeoff. Numerical simulations over a Gilbert-Elliott channel model and a Fritchman model show significant performance gains over previously proposed low-delay codes and random linear codes for certain range of channel parameters.

We study near optimal error correction codes for real-time communication. In our setup the encoder must operate on an incoming source stream in a sequential manner, and the decoder must reconstruct each source packet within a fixed playback deadline of T packets. The underlying channel is a packet erasure channel that can introduce both burst and isolated losses. We first consider a class of channels that in any window of length T +1 introduce either a single erasure burst of a given maximum length B, or a certain maximum number N of isolated erasures. We demonstrate that for a fixed rate and delay, there exists a tradeoff between the achievable values of B and N, and propose a family of codes that is near optimal with respect to this tradeoff. We also consider another class of channels that introduce both a burst and an isolated loss in each window of interest and develop the associated streaming codes. All our constructions are based on a layered design and provide significant improvements over baseline codes in simulations over the Gilbert-Elliott channel.

Streaming erasure codes encode a source stream to guarantee that each source packet is recovered within a fixed delay at the receiver over a burst-erasure channel. This paper introduces diversity embedded streaming erasure codes (DE-SCo), that provide a flexible trade-off between the channel quality and receiver delay. When the channel conditions are good, the source stream is recovered with a low delay, whereas when the channel conditions are poor the source stream is still recovered, albeit with a larger delay. Information theoretic analysis of the underlying burst-erasure broadcast channel reveals that DE-SCo achieve the minimum possible delay for the weaker user, without sacrificing the performance of the stronger user. A larger class of multicast streaming erasure codes (MU-SCo) that achieve optimal tradeoff between rate, delay and erasure-burst length is also constructed.