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ECG is the quickest and significant method identified to study one’s heart condition. It has also been recognized as one of the most accurate methods.

A deep neural network to identify irregular cardiac rhythms from single lead ECG signals at a significant performance and diagnostic yield is being developed which can reduce the load of cardiologists. Our neural network can identify up to 7 arrhythmias along with sinus rhythm. It is correlated to with clinically validated reports to enhance the output.

The network developed is a convolutional ANN which takes raw ECG signals as input that are sampled at a frequency of 250 Hz (250 samples per second). The network takes only ECG signals as input that are pre processed before fed to the network. This architecture contains 4 layers. The dataset contains over a lakh of patients > 18 years of age from various demographic ratios, whose cardiac rhythms have been obtained using Biocalculus.

Connectivity

    • USB –    Version 2.0
    • BLE(Bluetooth Low Energy) –   Bluetooth 5v.

Performance Characteristics

    • ECG Channel –   Single Channel
    • Input Dynamic Range –   10mV Peak-to-Peak.
    • On Board Memory –   4GB
    • Memory Type –   EEPROM.
    • Shelf Life –  Estimated 1Years

Circuitry Characteristics

    • Frequency Response –   0.5Hz to 40Hz
    • CMRR –   76dB
    • Input Impedance –   >100MOhm
    • Differential Range –   +/-5mV.
    • A/D Sampling Rate –   256/512 Samples/second
    • Resolution –   16bit
    • DC Offset Correction –   +/-300mV

Physical Characteristics

    • Dimension –   74×28×10MM
    • Weight –    20gm Inculding Battery

Power Requirements

    • Battery Type –    Rechargeable Lithium-Polymer Battery
    • Battery Life –    Continuous usage up to 4 days in a single charge
    • Battery Capacity –   300mAhv
    • Battery Charger Power Requirement –    100-240,50/60Hz,5WVAV
    • Battery Voltage –    3.7 Volts

Environmental Specification

    • Operational Temperature –    +10 to +45 degrees C
    • Operational Humidity –    10% to 95%(non condensing)
    • Storage Temperature –    -20 to +60 degrees C
    • Storage Humidity –    10% to 95%(non condensing)