Using ECG signal as an entropy source for efficient generation of long random bit sequences

Electrocardiogram (ECG) signal produced by the human heart has been investigated as a potential entropy source for cryptographic random bit generation for a long time. The throughput of existing methods remains as the bottleneck for its deployment in practical applications. To overcome this problem, we develop a Bernoulli entropy source by processing a single heartbeat ECG signal to obtain a long random bit sequence (RBS). The proposed method converts the signal into an IID (independent and identically distributed) source of entropy using efficient interpolation and optimization techniques. Several heartbeat signals, obtained from two different databases, were used to test the entropy source generating RBSs with one million bits. The entropy source was evaluated with the latest NIST recommendation for IID source validation and it passed all recommended tests and the average min-entropy obtained from several heartbeat signals of different individuals was close to the perfect entropy value of 1.0. It was also observed that the entropy increases monotonically with the increase of the length of keys. The proposed method can efficiently produce a long RBS for cryptographic applications, such as key generation for one-time pad and image encryption. The method could be further explored to generate a true random number with a personalized signature, which is crucial for information security in the future generation of computing.