Biosignal
A biosignal, also referred to as a biomedical signal, is any measurable signal originating from a biological system or living organism that reflects physiological processes, such as electrical, mechanical, acoustic, or chemical phenomena generated by cellular activities like ion flows across membranes during depolarization and repolarization.[1] These signals are typically weak, stochastic, and prone to noise from external interferences or motion artifacts, necessitating amplification and conditioning for reliable detection. Biosignals encompass a diverse range of types categorized by their physical nature and source, with bioelectrical signals being among the most prominent due to their direct measurability via electrodes. Common examples include the electrocardiogram (ECG), which records the heart's electrical activity to assess cardiac rhythm and detect arrhythmias; the electroencephalogram (EEG), capturing brain wave patterns for evaluating neurological conditions like epilepsy or sleep disorders; the electromyogram (EMG), measuring muscle electrical potentials to study neuromuscular function; the electrooculogram (EOG), tracking eye movements for gaze analysis; and the electrodermal activity (EDA), monitoring skin conductance changes indicative of autonomic nervous system responses to stress or arousal.[2] Other notable categories involve biomechanical signals like ballistocardiography for heart-induced body vibrations, bioacoustic signals such as heart sounds (phonocardiogram), and biochemical signals including pH or glucose levels in bodily fluids.[1] In biomedical engineering, biosignals play a pivotal role in healthcare applications, from non-invasive diagnostics and real-time patient monitoring to advanced interventions like brain-computer interfaces and prosthetic control. Processing techniques—such as filtering to remove noise, time-frequency analysis for feature extraction, and machine learning algorithms for classification—enable the transformation of raw biosignals into actionable insights, supporting early disease detection, personalized medicine, and rehabilitation therapies.[3] As of 2025, ongoing advancements, including wearable sensors and artificial intelligence integration such as generative models, continue to enhance the accuracy and accessibility of biosignal-based systems.[4][5]Fundamentals
Definition
Biosignals are defined as any signals originating from biological sources that represent physiological or biochemical processes in living organisms. These signals serve as measurable indicators of biological events, capturing space, time, or space-time records of activities within the body, such as the coordinated functions of organs, tissues, or cells.[6][7] In contrast to abiotic signals generated by non-living environmental or physical phenomena, biosignals are inherently stochastic and time-variant due to the dynamic nature of biological systems, with their characteristics influenced by inherent physiological variability across individuals and conditions. This variability arises from factors such as age, health status, and external influences, making biosignals non-stationary and challenging to model deterministically.[8][9][6] Illustrative examples include the mechanical signal produced by a heartbeat, which reflects cardiac contractions, or the electrical signal from neural impulses, which convey information across the nervous system; these highlight the diverse forms biosignals can take without overlapping into specific classifications.[10]Characteristics
Biosignals exhibit non-stationarity, where their statistical properties evolve over time in response to changing physiological states, such as variations in heart rate or neural activity.[11] This dynamic behavior complicates long-term analysis and modeling. Additionally, biosignals typically feature a low signal-to-noise ratio (SNR), arising from weak physiological origins overwhelmed by environmental interference and biological noise.[1] They also display multimodality, as signals from multiple physiological sources—such as cardiac and respiratory activities—often overlap, leading to composite recordings that blend distinct components.[1] The amplitude of electrical biosignals generally spans microvolts to millivolts, reflecting the subtle nature of bioelectric potentials generated by cellular ion flows. For instance, electrocardiogram (ECG) signals show a peak-to-peak amplitude of approximately 1 mV.[12] These low magnitudes underscore the sensitivity required in acquisition systems to detect meaningful patterns without amplification-induced distortion. Frequency spectra of biosignals are predominantly low-frequency, ranging from 0.05 to 100 Hz for common types like ECG and electroencephalogram (EEG), aligning with the timescales of physiological events such as heartbeat cycles or brain wave oscillations.[13] However, these spectra are prone to contamination by artifacts, including motion-induced fluctuations and electromagnetic interference, which introduce broadband noise across similar frequency bands.[1] Biosignals are inherently stochastic, modeled as random processes to account for their probabilistic fluctuations driven by underlying biological variability.[14] This variability stems from inter-individual differences in physiology, as well as influences like age and health status, which alter signal morphology and intensity—such as reduced amplitude in ECG traces among older adults.[11] A key challenge with biosignals is their rapid degradation outside the body due to attenuation and external noise, demanding real-time measurement to preserve fidelity.[15] Consequently, effective noise reduction through preprocessing is essential to isolate relevant features.[1]Types
Electrical Biosignals
Electrical biosignals arise from bioelectric potentials generated by the movement of ions, such as sodium (Na⁺), potassium (K⁺), calcium (Ca²⁺), and chloride (Cl⁻), across cell membranes in biological tissues. These potentials result from concentration gradients maintained by active transport mechanisms like the sodium-potassium pump, creating a resting membrane potential typically around -70 mV in neurons and myocytes. The equilibrium potential for a specific ion species is described by the Nernst equation:E = \frac{RT}{zF} \ln\left(\frac{[ion]_{out}}{[ion]_{in}}\right),
where R is the gas constant, T is the absolute temperature, z is the ion's valence, F is Faraday's constant, and [ion]_{out} and [ion]_{in} are the extracellular and intracellular ion concentrations, respectively.[16] This equation quantifies the electrical driving force for each ion at equilibrium, underpinning the initiation of electrical activity when membrane permeability changes.[1] At the biophysical level, electrical biosignals stem from action potentials in excitable cells like neurons and muscle cells, where rapid depolarization and repolarization occur due to voltage-gated ion channels. During an action potential, an influx of Na⁺ through opening sodium channels depolarizes the membrane, followed by K⁺ efflux through potassium channels to restore the resting state, propagating the signal along the cell. The seminal Hodgkin-Huxley model, developed from voltage-clamp experiments on squid giant axons, provides a qualitative framework for this process by incorporating time- and voltage-dependent conductances for Na⁺ and K⁺ ions, along with a leak current, to simulate the nonlinear dynamics of membrane excitability without requiring full ionic concentration details.[17] This model highlights how small perturbations can trigger regenerative feedback, leading to the all-or-none nature of action potentials observed in tissues.[18] Common examples of electrical biosignals include the electrocardiogram (ECG), which records cardiac electrical activity from the synchronized depolarization and repolarization of atrial and ventricular myocytes, producing the characteristic PQRST waveform where the P wave reflects atrial contraction, QRS indicates ventricular depolarization, and T wave shows repolarization.[1] The first practical ECG recording was achieved in 1901 by Willem Einthoven using his string galvanometer, a milestone that enabled non-invasive heart monitoring and earned him the 1924 Nobel Prize in Physiology or Medicine.[19] The electroencephalogram (EEG) captures brain electrical activity as summed postsynaptic potentials from cortical neurons, with typical scalp amplitudes of 10-100 μV and frequencies ranging from 0.5-100 Hz, encompassing delta (0.5-4 Hz, deep sleep), theta (4-8 Hz, drowsiness), alpha (8-12 Hz, relaxed wakefulness), and beta (13-30 Hz, active cognition) rhythms.[20] The electromyogram (EMG) measures muscle electrical activity from motor unit action potentials during contractions, reflecting the recruitment of skeletal muscle fibers with amplitudes up to several millivolts and frequencies of 20-500 Hz.[1] The electrooculogram (EOG) records eye movements by measuring corneo-retinal potentials using electrodes around the eyes.[2] The electrodermal activity (EDA) monitors changes in skin conductance due to sweat gland activity, reflecting autonomic nervous system responses.[2] These signals are typically acquired via surface electrodes placed on the skin, converting ionic currents to measurable voltages.[16]