Biocalculus

Detects:

• Normal Sinus Rhythm.
• Tachycardia/ Bradycardia.
• Atrial fibrillation.
• Atrial Flutter.
• Premature Ventricular contraction.
• Premature Atrial contraction.
• Pause.
• Cardiac Heart Block.

Intended for:

• Patients with infrequent symptoms
• Patients with intermittent chest pain
• Suspected asymptomatic patients
• Post-stroke monitoring
• Post-procedure monitoring

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