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.
- USB – Version 2.0
- BLE(Bluetooth Low Energy) – Bluetooth 5v.
- ECG Channel – Single Channel
- Input Dynamic Range – 10mV Peak-to-Peak.
- On Board Memory – 4GB
- Memory Type – EEPROM.
- Shelf Life – Estimated 1Years
- 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
- Dimension – 74×28×10MM
- Weight – 20gm Inculding Battery
- 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
- 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)