Your breathing pattern is as unique as a fingerprint

Every person possesses a distinct pattern of breathing through their nose that remains stable over long periods of time and functions like a biological signature. By tracking how individuals inhale and exhale over twenty-four hours, a new study demonstrates that these unique respiratory patterns can identify people with near perfect accuracy while also predicting individual levels of anxiety, depression, and body mass. The research was published in the journal Current Biology.

Breathing frequently seems like an automatic and straightforward physical process. Many people only notice their respiration when they are out of breath or engaged in strenuous exercise. Yet the act of pulling air into the body and pushing it out is governed by an extensive and intricate neural network.

This neural network operates primarily from the brainstem. It functions as a biological pacemaker that continuously adjusts human breathing to meet physiological needs. The system takes in vast amounts of sensory information from throughout the body to regulate the speed and depth of each inhalation and exhalation.

Because human brains display individual uniqueness in their wiring and activity, researchers suspected that the biological outputs generated by these local brain networks might also reflect high individuality. To pursue this idea, researchers from the Weizmann Institute of Science in Israel designed an experiment to track exactly how air moves through the nose over long durations.

Timna Soroka and Noam Sobel, the lead researchers on the project, chose to focus on the nose rather than the mouth. The nasal passages have a special relationship with the brain, packed with sensory nerves that send constant feedback regarding airflow. The brain actively controls this process, systematically alternating which nostril does the bulk of the breathing work.

To capture these long-term breathing patterns, the team developed a specialized wearable device. This small tracker rested on the back of the volunteer’s neck and connected to a nasal cannula, which is a thin plastic tube with two small prongs that sit just inside the nostrils.

Unlike standard medical tests that monitor breathing for just a few minutes to check lung capacity, this setup recorded respiration continuously for an entire day and night. The device contained highly sensitive pressure sensors that measured airflow independently for the left and right nostrils in real time. It recorded data six times per second, capturing tiny dynamic fluctuations in air movement.

The study involved roughly one hundred healthy participants, mostly in their twenties. Each person wore the tracker for a full twenty-four hours as they went about their daily lives, logging their basic activities and sleep schedules on a provided smartphone application.

For a subset of over forty participants, the research team repeated the process entirely. These individuals returned to wear the recording device for a second twenty-four hour period. The gap between the first and second recording sessions ranged from a few days to nearly two years in length.

When the researchers fed the raw breathing data into a computational model, they found that they could identify individuals with remarkable precision. Based on waking breathing patterns alone, the system correctly identified specific individuals out of the group with ninety-six point eight percent accuracy.

The success rate of this identification process places respiratory patterns on nearly the same level as established biometric markers like voice recognition. The data showed that human breathing is not just a general mammalian rhythm, but an individualized behavioral signature.

This ability to recognize a person based on their airflow held true even after long stretches of time. When the computer model learned a person’s breathing pattern on their first day of testing, it could still successfully pick them out of the crowd using data collected up to twenty-three months later.

To ensure the computer was looking at the actual act of breathing and not just distinct patterns of physical movement, the researchers also analyzed data from a motion sensor embedded within the device. While bodily movement allowed for some level of identification, it was vastly inferior to the accuracy achieved by analyzing the nasal airflow.

The researchers evaluated dozens of different parameters within the breathing data to verify these results. They grouped data into metrics like the volume of air inhaled, the length of pauses between breaths, and the asymmetry of airflow between the left and right nostrils.

No single feature was capable on its own to distinguish one person from another. The high level of overall identification accuracy required the computational model to look at roughly twenty to one hundred different breathing characteristics working in tandem.

Beyond simple human identification, the researchers assessed whether these respiratory signatures revealed anything regarding a person’s physical state. They broke the raw data down to look at physiological markers, such as the transition from being awake to being asleep.

The analytical data showed dramatic shifts between waking and sleeping states. When participants fell asleep, the overall volume of air they breathed in and out dropped, while the shifting of dominance between the right and left nostrils increased. By analyzing exactly five minutes of a person’s breathing data, the model could easily categorize whether they were asleep or awake.

The continuous airflow data also mathematically aligned with the participants’ body mass index, a standard calculation based on human height and weight. The research team noted a mathematical relationship between a person’s body mass and specific aspects of their nasal cycle, suggesting the neural dynamics driving respiration interact directly with body composition.

In addition to monitoring physiology, the researchers wanted to explore if these breathing patterns reflected specific aspects of human cognition and emotion. All the participants had completed standard psychological questionnaires to assess their baseline levels of anxiety, depressive symptoms, and behavioral traits associated with the autism spectrum.

Even though the study group consisted of typical adults without severe clinical diagnoses, their measured breathing patterns correlated with their survey scores. Researchers discovered they could partially predict an individual’s score on a depression inventory based entirely on features of their respiration, such as the peak speed of their inhalations during waking hours.

Similar predictive relationships emerged for general anxiety. Participants who scored higher on a trait anxiety assessment tended to exhibit slightly shorter inhale durations while they slept. Minor variations in the length of pauses between breaths were also associated with differing levels of self-reported anxiety.

When looking at the autism spectrum questionnaire, the data again highlighted mathematical associations tied to how participants breathed. Minute variations in how long a person paused during their inhalations corresponded to differing behavioral scores. These findings indicate that emotional and cognitive states leave faint but readable biological imprints on how our brainstems regulate our breathing.

While the study introduces a novel way to measure basic human biology, the experimental method has a few recognizable limitations. The nasal cannula occasionally slipped out of place while participants were sleeping, which interrupted the nighttime data collection.

Additionally, pressure-based sensors sitting inside the nose are excellent at measuring the precise timing of a breath, but they lack absolute perfection when calculating the total volume of air moving into the lungs. The physical appearance of the device could also limit its everyday usefulness, as wearing medical tubes on the face is highly visible.

Looking forward, the researchers plan to expand this testing methodology to broader populations. Because respiratory patterns offer direct insight into brain function, the research team envisions applying this approach to study various diseases. Monitoring a patient’s unique breathing fingerprint over long durations could eventually serve as a passive, non-invasive tool for tracking general neurological health.

The study, “Humans have nasal respiratory fingerprints,” was authored by Timna Soroka, Aharon Ravia, Kobi Snitz, Danielle Honigstein, Aharon Weissbrod, Lior Gorodisky, Tali Weiss, Ofer Perl, and Noam Sobel.