Recently,　pervasive　sensing　technologies　have　been　widely　applied　to　comprehensive　patient　monitoring　in　order　to　improve　clinical　treatment.　Various　types　of　biomedical　signals　collected　by　different　sensing　channels　provide　different　aspects　of　patient　health　information.　However,　due　to　the　uncertainty　and　variability　in　clinical　observation,　not　all　the　channels　are　relevant　and　important　to　the　target　task.　Thus,　in　order　to　extract　informative　representations　from　multi-channel　biosignals,　channel　awareness　has　become　a　key　enabler　for　deep　learning　in　biosignal　processing　and　has　attracted　increasing　research　interest　in　health　informatics.　Towards　this　end,　we　propose　FusionAtta　deep　fusional　attention　network　that　can　learn　channel-aware　representations　of　multi-channel　biosignals,　while　preserving　complex　correlations　among　all　the　channels.　FusionAtt　is　able　to　dynamically　quantify　the　importance　of　each　biomedical　channel,　and　relies　on　more　informative　ones　to　enhance　feature　representation　in　an　end-to-end　manner.　We　empirically　evaluated　FusionAtt　in　two　clinical　tasks:　multi-channel　seizure　detection　and　multivariate　sleep　stage　classification.　Experimental　results　showed　that　FusionAtt　consistently　outperformed　the　state-of-the-art　models　in　four　different　evaluation　measurements,　demonstrating　the　effectiveness　of　the　proposed　fusional　attention　mechanism.