An electrocardiogram (ECG) signal algorithm typically involves several steps aimed at processing and analyzing the electrical activity of the heart. Here's a general description of the algorithm: Signal Acquisition: The ECG signal is acquired using electrodes placed on the patient's skin. Typically, at least three electrodes are used to record a standard ECG: one on each arm and one on the left leg. The electrodes detect the electrical impulses generated by the heart. Signal Preprocessing: Filtering: The raw ECG signal may contain noise from various sources such as muscle movement, power line interference, and electrode contact issues. Filtering techniques such as bandpass filtering are applied to remove noise while preserving the essential features of the ECG waveform. Baseline Correction: The baseline of the ECG signal may vary due to respiration or other physiological factors. Baseline wander correction techniques are applied to remove this baseline drift. Feature Extraction: Once the QRS complexes are detected, various features are extracted from the ECG signal. These features may include the duration of the QRS complex, the amplitude of the R wave, the RR interval (time between successive R peaks), and others. Feature extraction is crucial for diagnosing cardiac abnormalities and analyzing heart rate variability. Arrhythmia Detection: ECG signals are used to detect various cardiac arrhythmias such as atrial fibrillation, ventricular tachycardia, and atrioventricular block. Algorithms analyze the timing and morphology of the ECG waveform to identify abnormal rhythms. Arrhythmia detection algorithms may use pattern recognition techniques, machine learning algorithms, or rule-based methods. Interpretation: Once features and arrhythmias are detected, the ECG signal is interpreted to diagnose cardiac conditions. Interpretation may involve comparing the ECG signal to standard templates, analyzing specific intervals and waveforms, and considering the patient's clinical history. Clinical Decision Support: The final step of the algorithm involves providing clinical decision support based on the analysis of the ECG signal. This may include generating diagnostic reports, alerting healthcare providers to abnormal findings, and recommending further diagnostic tests or interventions. Overall, ECG signal algorithms play a critical role in the diagnosis and management of various cardiac conditions by processing and analyzing the electrical activity of the heart. QRS Detection: Peak Detection: The QRS complex represents ventricular depolarization and is the most prominent feature of the ECG signal. QRS detection algorithms identify the peaks of the QRS complexes. Thresholding: Various thresholding techniques are used to distinguish QRS complexes from noise and other features in the ECG signal. Adaptive Methods: Adaptive thresholding methods adjust the detection threshold.

1. VLSI IMPLEMENTATION OF LOSSLESS ECG
COMPRESSION ALGORITHM FOR LOW POWER
DEVICES
K . Dhanush-2100040260
N . Vivek -2100040273
Under the supervision of Dr. Sai Krishna Santosh
G

2. OUTLINE
Introduction​
Literature survey
​Methodology
Block Diagram
Flowcharts
Observation
References

3. INTRODUCTION
 Electrocardiogram (ECG) indicates the electrical activity of the heart
and it is the most commonly used method to monitor the heartbeat.
With wireless and wearable healthcare devices, long-term ECG data
can be recorded continuously for monitoring and diagnosis.
 However, collecting such a large amount of data requires excessive
transmission energy or storage capacity, which significantly
increases the cost of long-term ECG applications.
 Therefore, an effective and efficient data compression method for
ECG signals is required.
 Long-term electrocardiogram (ECG) monitoring requires high-ratio
lossless compression techniques to reduce data transmission
energy and data storage capacity.
 we have proposed a high-ratio ECG compression system with low
computational complexity.
VLSI Implementation of Lossless ECG Compression Algorithm for Low Power Devices
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4. LITERATURE SURVEY
• Especially for low power application-specific integrated circuit (ASIC)
designs or embedded system designs, low computational complexity and a
low number of variables can decrease the chip area, thus reducing the chip
cost and power consumption[1].
• To date, various lossless ECG compression methods have been proposed,
including algorithms such as[2] :
 data pulse code modulation (DPCM) + Golomb-Rice encoding
 adaptive linear predictor + modified Huffman encoding
 adaptive linear predictor + variable-length encoding
 short term linear predictor + fixed-length encoding
 adaptive linear predictor + Golomb-Rice encoding
• These algorithms are relatively simple, but they do not utilize sufficient ECG
morphologies hence causing poor CRs.
VLSI Implementation of Lossless ECG Compression Algorithm for Low Power Devices
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5. LITERATURE SURVEY
Some other methods achieve high CRs. Among which,
• Miaou and Chao[3] applied the combination of distortion-
constrained codebook replenishment (DCCR), set partitioning in
hierarchical tree (SPIHT), and bit-plane coding (BPC) algorithms,
but this method contains recursive operation and requires many
variables.
• Zhou[4] applied k-means cluster predictor + Huffman encoding, and
Tseng et al. applied Takagi-Sugeno fuzzy neural network +
arithmetic encoding, but they require multiply-accumulate operation
many times when predicting each point.
• Tsai[5] applied adaptive linear predictor + Golomb-Rice encoding,
and Rzepka applied selective and multichannel linear predictor +
asymmetric numeral systems encoding.
but they all require integer division operation hence increasing the
computational complexity
VLSI Implementation of Lossless ECG Compression Algorithm for Low Power Devices
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6. PREVIOUS WORKS
VLSI Implementation of Lossless ECG Compression Algorithm for Low Power Devices 6

7. METHOLOGY
• This study proposes a lossless ECG compression
algorithm based on dual-mode prediction with
context error modeling.
• Firstly, as the morphologies of the ECG change
over time, we divide the signal of each heartbeat
cycle into two regions.
• To achieve high prediction accuracy, a 1st order
linear predictor and a combination of the template
predictor and 3rd order linear predictor are applied
in the two regions respectively.
VLSI Implementation of Lossless ECG Compression Algorithm for Low Power Devices
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8. METHOLOGY
• Secondly, we introduce a context-based error
modeling module to the system, which cancels the
statistical bias of the prediction algorithm and further
improves the prediction accuracy.
• Thirdly, we modify the Golomb-Rice encoding
algorithm to adaptively encode the prediction errors,
while preserving a code space for packaging the
information that is necessary for prediction.
• We evaluate the proposed system by using the MIT-
BIH Arrhythmia Database (ARRDB).
VLSI Implementation of Lossless ECG Compression Algorithm for Low Power Devices
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9. BLOCK DIAGRAM
VLSI Implementation of Lossless ECG Compression Algorithm for Low Power Devices
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10. PREDICTION BLOCK
VLSI Implementation of Lossless ECG Compression Algorithm for Low Power Devices
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11. ERROR MODELLING
VLSI Implementation of Lossless ECG Compression Algorithm for Low Power Devices
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12. GOLOMB RICE ENCODING
VLSI Implementation of Lossless ECG Compression Algorithm for Low Power Devices
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13. OBSERVATION
• The proposed system is evaluated on the ARRDB.
Experiment results show that the proposed system
achieves a CR from 2.975 to 3.040 with memory
requirements as low as 444 to 14556 variables.
• The sampling frequency and resolution of the
example are 360Hz and 11-bit respectively, which are
the same as those of the ARRDB.
VLSI Implementation of Lossless ECG Compression Algorithm for Low Power Devices
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14. FUTURE WORK PLAN
November
• Designing compression algorithm
• Developing RTL code
December
• Verification and Testing
VLSI Implementation of Lossless ECG Compression Algorithm for Low Power Devices
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15. REFERENCES
• [1] Z. Lu, D. Youn Kim, and W. A. Pearlman, ‘‘Wavelet compression of
ECG signals by the set partitioning in hierarchical trees algorithm,’’ IEEE
Trans. Biomed. Eng., vol. 47, no. 7, pp. 849–856, Jul. 2000.
• [2] C.-I. Ieong, P.-I. Mak, C.-P. Lam, C. Dong, M.-I. Vai, P.-U. Mak, S.-H.
Pun, F. Wan, and R. P. Martins, ‘‘A 0.83-µW QRS detection processor
using quadratic spline wavelet transform for wireless ECG acquisition in
0.35-µm CMOS,’’ IEEE Trans. Biomed. Circuits Syst., vol. 6, no. 6, pp.
586–595, Dec. 2012.
• [3] C. J. Deepu and Y. Lian, ‘‘A joint QRS detection and data compression
scheme for wearable sensors,’’ IEEE Trans. Biomed. Eng., vol. 62, no. 1,
pp. 165–175, Jan. 2015.
• [4] A. Koski, ‘‘Lossless ECG encoding,’’ Comput. Methods Programs
Biomed., vol. 52, no. 1, pp. 23–33, Jan. 1997.
• [5] E. Chua and W.-C. Fang, ‘‘Mixed bio-signal lossless data compressor
for portable brain-heart monitoring systems,’’ IEEE Trans. Consum.
Electron., vol. 57, no. 1, pp. 267–273, Feb. 2011.
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16. THANK YOU